Work information processing device, work information processing method, and program

Abstract

To provide a work information processing device capable of classifying each store taking into account the situation of each store and easily comparing work performance between stores belonging to the same group.SOLUTION: The work information processing device is a device including a processor that processes information about the work done in stores. The processor is configured to execute the steps: acquiring the actual work efficiency value of the task at multiple stores calculated for each store; determining a standard store characteristic, which is the store characteristic that serves as the basis for classifying multiple stores into multiple store groups out of the multiple store characteristics that indicate the characteristics of each of the multiple stores for each task; classifying multiple stores into multiple store groups for each task based on the standard store characteristic; and displaying work efficiency performance information showing the actual work efficiency of each store for each task and store group in a manner identifiable each store on a display device.SELECTED DRAWING: Figure 4

Description

Technical Field

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[0001] The present disclosure relates to a work information processing apparatus, a work information processing method, and a program.

Background Art

[0002] Conventionally, there has been known a management support system that can extract and aggregate information regarding business performance and cost management in each store, and analyze such information to present, in real time and in a comparable manner with other stores, information regarding the management state of each store to a user (see Patent Document 1). This management support system includes a plurality of user terminals used by a plurality of different users, and a server that communicates with the plurality of user terminals. The user terminal transmits user information including information regarding business performance in the user's store and information regarding cost management in the user's store to the server. The server analyzes the user information based on a predetermined analysis rule to obtain management information regarding the user's store and transmits it to the plurality of user terminals. The user terminal displays, based on a user's instruction, the management information in the stores of a plurality of users as a graph that can be compared between the stores. In the graph, management information including the difference or ratio between the average sales situation in the stores of a plurality of users and the sales situation of each store, and the difference or ratio between the sales or sales achievement rate in the same period of the previous year in each store and the current sales or sales achievement rate in the current year is visually distinguishable and displayed between the stores. In the display of the management information, management information in a specific store selected from among the stores of a plurality of users and in a plurality of stores within a peripheral area at a predetermined distance from the location of the specific store is displayed so as to be comparable between the stores.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The management support system described in Patent Document 1 does not consider the work efficiency of each task at each store, which is a more detailed perspective than the management perspective. Furthermore, while the comparison of information between stores takes into account whether each store belongs to the same group chain, it does not take into account the group classification based on store characteristics that affect work efficiency. Therefore, even if the store conditions (e.g., the size of the sales floor or the level of congestion in the sales floor) are different, stores are classified into the same group and management information is compared between stores, making it difficult to compare actual results that take into account the store conditions.

[0005] This disclosure is made in view of the above circumstances and provides a work information processing device, a work information processing method, and a program that classify stores into groups taking into account the circumstances of each store and allow for easy comparison of work performance among stores belonging to the same group. [Means for solving the problem]

[0006] One aspect of the present disclosure is a work information processing device for processing information related to work performed in a store, comprising a processor, the processor, for each work, acquires actual work efficiency values ​​for the work in a plurality of stores for each of the plurality of stores, for each work, determines a reference store characteristic which is a store characteristic that serves as a basis for classifying the plurality of stores into a plurality of store groups from among a plurality of store characteristics that represent the characteristics of each of the plurality of stores, and for each work, Decision made The aforementioned standard store characteristics The categories or specific values ​​included Based on this, the multiple stores are classified into the multiple store groups, and for each task and each store group, work efficiency performance information showing the actual work efficiency of each store is displayed on a display device in a way that allows each store to be identified. In determining the standard store characteristics, (i) for each task, a correlation coefficient is calculated showing the correlation between the actual value of the task efficiency and each store characteristic, and the store characteristics for which the absolute value of the correlation coefficient is equal to or greater than a predetermined threshold are determined as the standard store characteristics, or (ii) for each task, a regression model is trained to derive the task efficiency from the multiple store characteristics, and based on the importance of each store characteristic, the store characteristics for which the importance is equal to or greater than a predetermined threshold, or the store characteristics corresponding to a predetermined number of importance levels from the top, are determined as the standard store characteristics. It is a work information processing device.

[0007] One aspect of this disclosure is, A work information processing device equipped with a processor, A work information processing method for processing information related to work performed in a store, The aforementioned processor, For each of the aforementioned tasks, the steps include obtaining the actual work efficiency values ​​for each of the multiple stores, The aforementioned processor, For each of the above operations, the steps include determining a standard store characteristic, which is a store characteristic that serves as a criterion for classifying the multiple stores into multiple store groups, from among multiple store characteristics that represent the characteristics of each of the multiple stores, The aforementioned processor, For each of the aforementioned tasks, Decision made The aforementioned standard store characteristics The categories or specific values ​​included Based on this, the steps are to classify the plurality of stores into the plurality of store groups, The aforementioned processor, The process includes the step of displaying on a display device, in a way that allows each store to be identified, work efficiency performance information, which shows the actual work efficiency value of each store, for each of the aforementioned tasks and for each of the aforementioned store groups. The step of determining the standard store characteristics includes: (i) for each task, calculating a correlation coefficient showing the correlation between the actual value of the task efficiency and each store characteristic, and determining the store characteristics for which the absolute value of the correlation coefficient is equal to or greater than a predetermined threshold as the standard store characteristics; or (ii) for each task, training a regression model that derives the task efficiency from the multiple store characteristics, and determining the store characteristics for which the importance of each store characteristic is equal to or greater than a predetermined threshold, or store characteristics corresponding to a predetermined number of importance levels from the top, as the standard store characteristics. This is a method for processing work information.

[0008] One aspect of this disclosure is a program for causing a computer to perform each step of the above-described work information processing method. [Effects of the Invention]

[0009] According to this disclosure, by classifying each store into groups based on the circumstances of each store, it is possible to easily compare work performance between stores belonging to the same group. [Brief explanation of the drawing]

[0010] [Figure 1] Block diagram showing an example configuration of the work information processing system in the embodiment. [Figure 2A] Block diagram showing an example configuration of a work information processing device. [Figure 2B] Block diagram showing an example of terminal configuration [Figure 3A] A diagram showing an example of store characteristics information. [Figure 3B] A diagram showing an example of work item information. [Figure 3C] A diagram showing an example of work effort information. [Figure 3D] A diagram showing an example of shipping information. [Figure 3E] A diagram showing an example of picking instruction information. [Figure 3F] A diagram showing an example of material preparation instruction information. [Figure 4]Flowchart showing an operation example of a work information processing device [Figure 5] Figure for explaining an example of calculating the RE actual value [Figure 6] Figure showing an example of determining store classification criteria [Figure 7] Figure showing an example of determining store classification criteria using a correlation coefficient [Figure 8] Figure showing an example of determining store classification criteria using a regression model [Figure 9] Figure showing an example of displaying the RE actual value and the comparison reference value [Figure 10] Figure showing an example of a subdivided group in which store groups are subdivided and a comparison reference value for each subdivided group [Figure 11] Figure for explaining the generation of work schedules and work plans [Figure 12] Figure showing an example of store characteristics related to cashier work [Figure 13] Figure showing an example of store characteristics related to the manufacturing work of sushi trains

Mode for Carrying Out the Invention

[0011] Hereinafter, embodiments will be described in detail with reference to the drawings as appropriate. However, detailed descriptions that are more than necessary may be omitted. For example, detailed descriptions of well-known matters and descriptions of substantially the same configurations may be omitted. This is to avoid making the following description unnecessarily redundant and to facilitate the understanding of those skilled in the art. Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and it is not intended to limit the subject matter described in the claims thereby.

[0012] <Configuration of Work Information Processing System> FIG. 1 is a diagram showing a configuration example of a work information processing system 5 according to an embodiment of the present disclosure. The work information processing system 5 includes a work information processing device 10 and a terminal 20. The work information processing device 10 and the terminal 20 are communicably connected by wire or wirelessly.

[0013] The work information processing device 10 manages various tasks related to the online supermarket business. The work information processing device 10 processes information related to tasks performed in stores. Stores are, for example, stores where the products handled by the online supermarket are located. Tasks performed in stores include, for example, picking, packing, and material preparation, or may include other tasks. Picking tasks include at least one of picking fresh produce (also referred to as picking (fresh produce)) and picking non-fresh produce (also referred to as picking (non-fresh produce)). Packing tasks are tasks that pack products for which at least one of picking (fresh produce) and picking (non-fresh produce) has been performed. Material preparation tasks are tasks that prepare materials to be used for picking or packing. The work information processing device 10 may also manage various tasks related to businesses other than the online supermarket. For example, various tasks could include cashier duties, conveyor belt sushi preparation (cooking), cleaning (depending on the size of the facility, the degree of dirtiness, equipment, etc.), and manufacturing (depending on equipment, workspace size, skill level, etc.).

[0014] For example, the work information processing device 10 performs processing to support the visualization of work efficiency information for each task at each store. For example, the work information processing device 10 determines target values ​​for the future work efficiency of each task at each store, and based on these target values, it performs processing to generate future work plans and work plans for performing each task at each store. Work efficiency is shown, for example, by RE (Reasonable Expectancy). RE is the amount of work per person-hour (per person and per hour), and can be rephrased as work efficiency.

[0015] The work information processing device 10 may be, for example, a server device or a PC. The work information processing device 10 may be installed, for example, at the headquarters of the online supermarket, at stores or warehouses where the products handled by the online supermarket are kept, or at other locations.

[0016] Terminal 20 is carried by, for example, managers (e.g., people at headquarters or store managers) or workers who manage operations at the headquarters, stores, or warehouses of the online supermarket. Terminal 20 may be, for example, a mobile device, smartphone, tablet, or PC. Terminal 20 may be installed in a designated location or it may be portable and carried with the manager or worker.

[0017] Furthermore, terminal 20 may operate as an input / output device by working in conjunction with the work information processing device 10. For example, terminal 20 may input the input information required by the work information processing device 10 and transmit it to the work information processing device 10, and receive and present (e.g., display or audio output) the output information output by the work information processing device 10. For example, terminal 20 may display information on the work efficiency of each task at each store.

[0018] Figure 2A is a block diagram showing an example configuration of the work information processing device 10. The work information processing device 10 comprises a processor 11, a communication device 12, and a memory 13.

[0019] The processor 11 implements various functions by executing programs stored in memory 13. The processor 11 may include an MPU (Microprocessing Unit), a CPU (Central Processing Unit), a DSP (Digital Signal Processor), etc. The processor 11 may also be composed of various integrated circuits (e.g., LSI (Large Scale Integration), FPGA (Field Programmable Gate Array)). The processor 11 coordinates the operation of each part of the work information processing device 10 and performs various processes.

[0020] The communication device 12 transmits various data or information. The communication method used by the communication device 12 may be a WAN (Wide Area Network), a LAN (Local Area Network), cellular communication for mobile phones (e.g., LTE, 5G), or short-range communication (e.g., infrared communication or Bluetooth® communication).

[0021] Memory 13 includes primary storage devices (e.g., RAM (Random Access Memory) or ROM (Read Only Memory)). Memory 13 may also include secondary storage devices (e.g., HDD (Hard Disk Drive) or SSD (Solid State Drive)) or tertiary storage devices (e.g., optical discs or SD cards). Memory 13 may also include other storage devices. Memory 13 stores various data, information, programs, etc.

[0022] Figure 2B is a block diagram showing an example configuration of terminal 20. Terminal 20 comprises a processor 21, a communication device 22, a memory 23, an operation device 24, and a display device 25.

[0023] The processor 21 implements various functions by executing programs stored in memory 23. The processor 21 may include an MPU, CPU, DSP, etc. The processor 21 may also be composed of various integrated circuits (e.g., LSI, FPGA). The processor 21 coordinates the operation of each part of the terminal 20 and performs various processes.

[0024] The communication device 22 transmits various data or information. The communication method used by the communication device 22 may be WAN, LAN, cellular communication for mobile phones (e.g., LTE, 5G), or short-range communication (e.g., infrared communication or Bluetooth® communication).

[0025] Memory 23 includes primary storage devices (e.g., RAM or ROM). Memory 23 may also include secondary storage devices (e.g., HDD or SSD) and tertiary storage devices (e.g., optical discs or SD cards). Memory 23 may also include other storage devices. Memory 23 stores various data, information, programs, etc.

[0026] The operating device 24 may include various buttons, keys, touch panels, microphones, or other input devices. The operating device 24 accepts input of various data and information.

[0027] The display device 25 may include a liquid crystal display device, an organic EL display device, or other display devices. The display device 25 displays various data and information.

[0028] Next, we will explain the various types of information held by memory 13.

[0029] Memory 13 includes, for example, store characteristic information I1, work item information I2, work effort information I3, shipping information I4, picking instruction information I5, and material preparation instruction information I6.

[0030] Figure 3A shows an example of store characteristic information I1. Store characteristic information I1 is information about store characteristics that indicate the features of each store. Store characteristics may include, for example, the size of the store's sales floor (also simply called sales floor size), the level of congestion in the store's sales floor (also simply called sales floor congestion), the packing method used for packing work performed in the store (also simply called packing method), the size of the workspace where work is performed in the store (also simply called workspace size), the scale of orders for products sold in the store (also simply called order scale), etc. Store characteristic information I1 includes store ID (Identification), store name, and information on each store characteristic.

[0031] Figure 3B shows an example of work item information I2. Work item information I2 is information about work items that indicate what kind of work exists. Work item information I2 can exist for each work item (i.e., work). Work item information I2 includes the work ID and the work name.

[0032] Figure 3C shows an example of work effort information I3. Work effort information I3 includes information on the amount of work required for each task within each time period. Work effort information I3 includes information on the store ID, the work date on which the work is performed (e.g., expressed as year, month, and day), the time period on which the work is performed, and the amount of work required for each task. In Figure 3C, the amount of work is shown in man-hours; for example, the amount of work required for work ID "W0001" is shown to be 1 man-hour in all time periods. Work effort information I3 may also include information on past work performance or information on future work plans (instructions). Note that the amount of work may also be shown in terms of the number of people.

[0033] Figure 3D shows an example of shipping information I4. Shipping information I4 is information about the shipment of goods obtained through work. Shipping information I4 includes store ID, shipping ID, shipping date, shipping time, picking time slot (the time period during which picking work is performed), and work ID. This shipping information I4 may be information about actual shipments or information about planned (instructed) shipments.

[0034] Figure 3E shows an example of picking instruction information I5. Picking instruction information I5 is instruction information related to the picking operation. Picking instruction information I5 may exist for each store. Picking instruction information I5 includes information such as store ID, shipping ID, product ID, quantity of the product to be picked, and operation ID.

[0035] Figure 3F shows an example of material preparation instruction information I6. Material preparation instruction information I6 is instruction information related to material preparation work. Material preparation instruction information I6 includes information such as the store ID, the work date on which the material preparation work will be carried out (e.g., expressed as year, month, and day), the deadline for the material preparation work, and the number of materials to be prepared by the material preparation work (quantity to be prepared). For example, by referring to Figure 3C, it is possible to determine whether or not work is being done for each time period, and the man-hours spent up to the deadline in Figure 3F correspond to the quantity to be prepared in Figure 3F.

[0036] The information stored in memory 13 is used, for example, to visualize information regarding the work efficiency and work objectives of each task, and to generate work plans and work schedules to achieve the work objectives.

[0037] Next, an example of the operation of the work information processing device 10 will be described. Figure 4 is a flowchart showing an example of the operation of the work information processing device 10.

[0038] First, the processor 11 acquires various performance values ​​for each task at each store (S11). These performance values ​​include the identification information for each task, the actual man-hours of the worker who performed the task (also called actual man-hours), and the actual workload for each task (also called actual workload). One man-hour is the amount of work performed by one person in one hour. For example, the processor 11 may receive the various performance values ​​for each task from the store terminals located in each store via a communication device. The processor 11 may receive the various performance values ​​for each task from the store terminals via a communication device 12.

[0039] Processor 11 calculates the actual RE value (actual RE value) for each task at each store based on the various actual values ​​of each task at each store obtained (S12). For example, processor 11 may calculate the actual RE value for each task by dividing the actual amount of work included in the various actual values ​​of each task by the actual man-hours. An example of calculating the actual RE value will be described later.

[0040] The processor 11 may obtain the RE performance value by means other than calculating it. For example, the RE performance value may not be calculated in the work information processing device 10, but may be calculated by the store terminal. In this case, the processor 11 may obtain the calculated RE performance value from the store terminal via the communication device 12.

[0041] For each task, the processor 11 determines a store characteristic (also called a store classification criterion or standard store characteristic) from among multiple store characteristics to serve as a standard for classifying multiple stores into multiple store groups (S13). Details of the store classification criterion determination will be described later.

[0042] For each task, processor 11 classifies multiple stores according to store classification criteria and generates multiple store groups to which one or more stores belong (S14). For example, processor 11 may classify multiple stores and generate multiple store groups based on the store characteristics of each store. Alternatively, processor 11 may classify multiple stores and generate multiple store groups based on store characteristics and RE performance values. Details of store group generation examples will be described later.

[0043] The processor 11 determines a comparison reference value for each task and for each store group, which is compared with the RE performance value of each store (S15). Details of the determination of the comparison reference value will be described later. The processor 11 displays the acquired RE performance value and the determined comparison reference value on the display device 25 of the terminal 20 for each task and for each store group (S16). Details of the display of RE performance value and comparison reference value will be described later.

[0044] The processor 11 determines the RE target value for each task and for each store group (S17). Details of the RE target value determination will be described later. The processor 11 may determine the RE target value by calculation, or it may determine the RE target value by obtaining the RE target value input via the operation device 24 of the terminal 20.

[0045] The processor 11 generates (formulates) at least one of a work schedule and a task schedule for each store based on the RE target value (S18). Details of the generation examples of work schedules and task schedules will be described later.

[0046] Figure 5 is a diagram illustrating an example of RE (Revenue Average) calculation. The various actual values ​​for each task acquired by the work information processing device 10 from the terminal 20 include actual man-hours and actual workload. In Figure 5, tasks include, for example, picking (perishable goods), picking (non-perishable goods), packing, material preparation, etc. Figure 5 shows that the processor 11 calculates the RE actual value (e.g., 30 items / person-hour, 40 orders / person-hour) for each task based on the actual man-hours and actual workload.

[0047] Next, we will explain examples of how to determine store classification criteria. Figure 6 shows an example of how store classification criteria are determined.

[0048] Processor 11 determines the store classification criteria. Store classification criteria are store characteristics used to classify multiple stores from among the multiple store characteristics included in store characteristic information I1. For example, Figure 6 illustrates that in fresh food picking operations, three of the five store characteristics—store floor size, store floor congestion, and order volume—become store classification criteria. In Figure 6, the store classification criteria are indicated by circles. Similarly, in non-fresh food picking operations, three of the five store characteristics—store floor size, store floor congestion, and order volume—are determined to be store classification criteria. Furthermore, in packing and material preparation operations, two of the five store characteristics—packing method and workspace size—are determined to be store classification criteria.

[0049] The processor 11 may determine the store classification criteria by acquiring the store classification criteria entered via the operation device 24 of the terminal 20. In this case, the operation device 24 of the terminal 20 accepts the input of the store classification criteria from the user, and the communication device 22 transmits the input store classification criteria to the work information processing device 10. In the work information processing device 10, the processor 11 may determine the store classification criteria by acquiring the store classification criteria from the terminal 20 via the communication device 12. For example, store managers or headquarters administrators, acting as users, can select the most suitable store classification criteria through user input by recognizing how the characteristics of each store may affect which tasks.

[0050] Furthermore, the processor 11 may generate store classification criteria for each task based on the work characteristics. For example, the processor 11 may determine the store classification criteria using a correlation coefficient based on past RE performance values ​​for each task in each store.

[0051] Figure 7 shows an example of determining store classification criteria using correlation coefficients. Past RE performance values ​​for each task at each store are stored, for example, in memory 13 and retrieved from memory 13 by the processor 11. For each task, the processor 11 calculates a correlation coefficient that shows the correlation between store characteristics and RE performance values, based on the store characteristics and RE performance values ​​of each store. The stronger the correlation (stronger the relationship) between store characteristics and RE performance values, the larger the absolute value of the correlation coefficient. Store characteristics with respect to RE performance values ​​that have a large absolute value of the correlation coefficient can be said to be store characteristics that contribute to work efficiency. Based on the calculated correlation coefficient, the processor 11 determines store classification criteria from among multiple store characteristics. In this case, the processor 11 may use a threshold th1 for store classification to determine store classification criteria from among multiple store characteristics depending on whether the correlation coefficient is greater or less than the threshold th1.

[0052] In Figure 7, each task is mapped onto a two-dimensional plane formed by the store characteristics of each store and the RE performance values ​​of each store. In Figure 7, the sales floor area and the past RE performance values ​​of picking (fresh produce) work (also simply called picking (fresh produce) RE), the sales floor congestion level and picking (fresh produce) RE, and the workspace area and picking (fresh produce) RE are mapped for each store. Each mapping point indicates the position of each store on this two-dimensional plane and corresponds to the store characteristic value of each store and the past RE performance value for a given task at each store. In Figure 7, the correlation coefficient between sales floor area and picking (fresh produce) RE is 0.95, the correlation coefficient between sales floor congestion level and picking (fresh produce) RE is -0.58, and the correlation coefficient between workspace area and picking (fresh produce) RE is 0.17. The processor 11 determines store characteristics whose correlation coefficient is above or below the threshold th1 as store classification criteria. For example, if the absolute value of the threshold th1 for store classification is set to 0.50, then store characteristics with a correlation coefficient of 0.50 or higher (e.g., sales floor size) and store characteristics with a correlation coefficient of -0.50 or lower (e.g., sales floor congestion) are determined as store classification criteria.

[0053] Furthermore, for example, the processor 11 may determine store classification criteria for each task using the importance of each store characteristic in a regression model that derives an RE value from multiple store characteristics. The regression model may be a linear regression equation as shown in (Equation 1) below, or it may use various machine learning methods such as decision trees, GBDT (Gradient Boosting Decision Tree), SVR (Support Vector Regression), or deep learning.

[0054] A regression model can be expressed as an equation that derives the appropriate RE value for a given task, for example, by using the normalized values ​​of each store's characteristics as variables. A regression model is established for each task. For example, the appropriate RE value E1 for picking (fresh produce) can be expressed by the following equation using each store's characteristics and coefficients. E1 = Coefficient 1 × Store floor space + Coefficient 2 × Store floor congestion level + Coefficient 3 × Number of orders + ... + Constant ... (Equation 1)

[0055] Note that while Equation 1 provides examples of up to three coefficients and store characteristics, in practice, many more store characteristics (for example, all or some of the store characteristics included in store characteristic information I1) and coefficients may be used.

[0056] The processor 11 may train a regression model (regression equation) based on the store characteristics of multiple stores. Through training, the processor 11 may calculate appropriate coefficients (coefficient 1, coefficient 2, ...) and constants in the regression model. For example, the processor 11 may substitute specific values ​​of the store characteristics of many stores and the RE performance values ​​obtained in the past at these stores into (equation 1) as the regression model, and train the regression model to derive (e.g., calculate) appropriate coefficients and constants. The coefficients derived here are values ​​that correspond to (e.g., are proportional to) the importance of each store characteristic assigned to the coefficient. Furthermore, if a nonlinear regression method such as a decision tree is used, the importance of each store characteristic may be calculated from the trained regression model or SHAP values. Therefore, the work information processing device 10 can calculate the importance of each store characteristic by training the regression model for each work, and can derive store characteristics that contribute greatly to the work characteristics.

[0057] Figure 8 shows an example of determining store classification criteria using a regression model. For each task, the processor 11 trains the regression model based on each store's characteristics and past RE performance values, and calculates appropriate values ​​for coefficients and constants in the regression model. The coefficients assigned to each store's characteristics in the regression model correspond to their importance for a given task. This importance can be said to indicate the degree of influence and contribution of store characteristics to work efficiency. For example, the processor 11 may determine a predetermined number of store characteristics (e.g., three) with the highest importance among the various store characteristics as store classification criteria.

[0058] In this way, the work information processing device 10 can use a regression model to derive the importance of store characteristics even when it is difficult for the user (e.g., an employee, store manager, or head office administrator) to recognize which store characteristics are important and to what extent, and can derive store classification criteria based on that importance.

[0059] Next, we will explain an example of creating a store group.

[0060] As explained earlier, the store characteristics information I1 in Figure 3A aggregates information on the classification of store characteristics for each store. For example, the classification of sales floor size is divided into two categories: large / small. The classification of sales floor congestion is divided into three categories: high / medium / low. The classification of packing method is divided into two categories: method A / method B. The classification of work area size is divided into two categories: large / small. The classification of order volume is divided into three categories: large / medium / small. Note that the store characteristics information I1 in Figure 3A is shown by broad classifications to which specific values ​​belong, but is not limited to this. The store characteristics information I1 may also be shown by specific values ​​for each store characteristic (for example, the area value indicating sales floor size, or the specific number of orders indicating order volume).

[0061] The store classification criteria determine the store characteristics used to classify stores. Based on the store classification criteria and store characteristic information I1 corresponding to the work characteristics, the processor 11 forms groups of stores that meet the criteria. In other words, for each task, the processor 11 generates multiple store groups such that one or more stores with the same classification of each store characteristic included in the store classification criteria belong to the same store group. Alternatively, the processor 11 may generate multiple store groups for each task such that one or more stores with the same or similar specific values ​​of each store characteristic included in the store classification criteria belong to the same store group.

[0062] For example, let's assume that store characteristic information I1 in Figure 3A is store characteristic information I1 for picking (fresh produce) operations. In this case, stores A and D have the same classification of the three store characteristics (sales floor size, sales floor congestion level, and order volume) that are the store classification criteria for picking (fresh produce) operations. Therefore, with respect to picking (fresh produce), stores A and D are classified into the same store group. On the other hand, stores A and B have different classifications of at least one (in this case, all three) of the three store characteristics that are the store classification criteria for picking (fresh produce) operations. Therefore, with respect to picking (fresh produce), stores A and B are classified into different store groups.

[0063] Furthermore, when the processor 11 generates a store group, if the number of stores included in the store group is less than a predetermined threshold, it may take the following actions. For example, the processor 11 may exclude stores with special store characteristics from the stores to be classified. Special store characteristics may be, for example, a store whose value of a predetermined store characteristic differs significantly from the value of the same predetermined store characteristic at other stores. For example, a store may have a different nearby facility (e.g., an amusement park, a stadium, an event venue) than other stores, resulting in a significantly larger volume of orders at that store compared to other stores. The processor 11 may also notify a warning that there are stores belonging to a store group with a number of stores less than the predetermined threshold th2, and may prompt the following actions, for example. For example, the processor 11 may re-determine the store classification criteria so that the number of stores belonging to a store group with a number of stores less than the threshold th2 is small (e.g., zero). In this case, the divisions within the already determined store classification criteria may be reset. For example, instead of dividing store congestion into three zones (high, medium, and low), it could be divided into two zones (high and low). This makes it more likely that stores will fall into the same congestion zone, making it easier for the store classification criteria to match and increasing the likelihood that they belong to the same store group.

[0064] In addition, when a warning is notified, the processor 11 may send an instruction to the terminal 20 to display the warning information via the communication device 12. Then, when the terminal 20 receives an instruction to display the warning information from the work information processing device 10 via the communication device 22, the processor 21 may display the warning information via the display device 25.

[0065] Next, we will explain an example of determining the reference value for comparison.

[0066] The comparison benchmark includes at least one of the following: top store statistics V1, headquarters recommended value V2, and store characteristic regression value V3. Top store statistics V1 is a statistical value (e.g., median, mean, maximum, minimum, etc.) of RE performance for stores that rank highly in a given statistical analysis among all stores. Headquarters recommended value V2 is a recommended value recommended by the headquarters of an online supermarket, etc. For example, headquarters recommended value V2 is the RE performance value that headquarters considers desirable for stores belonging to this store group. Store characteristic regression value V3 is an appropriate RE value calculated based on a regression model (regression equation). The regression model is calculated, for example, based on each store's characteristics and each store's RE performance value.

[0067] Let's explain the top store statistics V1 in detail. For example, the processor 11 may, for each task and for each store group, extract one or more stores from all the stores under comparison that meet the predetermined criteria for being a top store, and calculate the top store statistics V1 for each task by taking statistics on the RE performance values ​​of the one or more extracted stores.

[0068] For example, processor 11 may, for each task and for each store group, extract one or more stores from all comparable stores that are among the top N% with the largest RE performance values. Alternatively, processor 11 may, for each task and for each store group, extract the top N stores with the largest RE performance values ​​from all comparable stores. Alternatively, processor 11 may, for each task and for each store group, extract one or more stores (e.g., other stores) from all comparable stores that have obtained an RE performance value greater than that of a predetermined store (e.g., its own store). Alternatively, processor 11 may, for each task and for each store group, extract N stores from all comparable stores that have obtained an RE performance value greater than that of a predetermined store and with a small difference from that store's RE performance value, sorted in order of the smallest difference from that store's RE performance value. Alternatively, processor 11 may, for each task and for each store group, extract one or more stores from all comparable stores that have an RE performance value greater than that of a predetermined store and with a large RE performance value that is among the top N% with the largest RE performance values. Furthermore, the statistical value of the RE performance of one or more extracted stores may be the mean, median, lower limit, N-quantile, or any other statistical value of the RE performance of one or more extracted stores.

[0069] The value of "N" above is arbitrary and may be, for example, a value received from the user via the operating device 24 of terminal 20. The processor 11 may also obtain the value of N from terminal 20 via the communication devices 12 and 22.

[0070] The headquarters recommended value V2 is, for example, one of the comparison reference values ​​specified by the headquarters administrator. For example, the headquarters recommended value V2 may be a value received from the headquarters administrator via the operation device 24 of the terminal 20. The communication device 22 of the terminal 20 may transmit the input headquarters recommended value V2 to the work information processing device 10. In the work information processing device 10, the processor 11 may determine the headquarters recommended value V2 as a comparison reference value by obtaining the headquarters recommended value V2 from the terminal 20 via the communication device 12.

[0071] Furthermore, the top store statistics V1 is useful, for example, when a sufficient number of stores can be guaranteed as comparison stores, or when the stores can accept it because it is determined statistically. The headquarters recommended values ​​V2 are useful, for example, when the number of stores to be compared is small and the validity of the statistics cannot be guaranteed, or when it is appropriate to determine the comparison standard values ​​from a management perspective.

[0072] Furthermore, the processor 11 may generate a regression model for each task and calculate a store characteristic regression value V3 as a comparison criterion value by regressioning the RE value from each store characteristic based on the regression model. The regression model may be expressed by the same equation as (Equation 1) described above, for example. Also, if the store classification criteria have been determined before the comparison criterion value was determined, the regression model may be expressed using the store characteristics that are the store classification criteria and without using store characteristics that are not store classification criteria. In this case, for example, the regression model for picking (fresh produce) work may express the appropriate RE value E1 for picking (fresh produce) work by the following (Equation 1') using the three store characteristics that are the store classification criteria for the work and coefficients, etc. E1 = Coefficient 1 × Store floor space + Coefficient 2 × Store floor congestion level + Coefficient 3 × Number of orders + Constant ... (Equation 1') The learning method and processing method for the regression model in (Equation 1') are the same as in the case of (Equation 1) described above. Alternatively, the processor 11 may calculate the appropriate RE value for each store, rather than for each store group, and determine the store characteristic regression value V3 for each store.

[0073] Equation 1 or Equation 1', as a regression model, is shown as a multiple regression equation, but is not limited to this. For example, the processor 11 may use various machine learning techniques to train a regression model for each task based on the store characteristics of each store and the RE performance values ​​of each task. Then, the processor 11 may use the trained regression model (trained model) to input one or more store characteristic values ​​as store classification criteria for each task and output an appropriate RE value. The machine learning techniques may include, for example, random forest, GBDT, deep learning, etc. In other words, the processor 11 may use AI (Artificial Intelligence) to determine a comparison criterion value as the store characteristic regression value V3 as the appropriate RE value.

[0074] The processor 11 may use the RE appropriate value directly as the store characteristic regression value V3, or it may calculate the store characteristic regression value V3 by multiplying the RE appropriate value by the improvement target rate (for example, +3%). The regression model may be a nonlinear regression model, not just a linear regression model. The processor 11 may also use a model other than the regression model to determine the comparison standard value.

[0075] Next, we will explain an example of how to display RE actual values ​​and comparison benchmark values. Figure 9 shows an example of displaying RE performance values ​​and comparison benchmark values. In Figure 9, an example of displaying RE performance values ​​and comparison benchmark values ​​is shown for store group G1, which shares common store characteristics such as a large sales floor area, high sales floor congestion, and a large number of orders.

[0076] The processor 11 displays the actual RE value AV for each store for each store group and each task. The processor 11 may also display the comparison reference value SV on the display device 25 for each store group and each task. In this case, the communication device 12 transmits display information including at least one of the actual RE value AV and the comparison reference value SV to the terminal 20. At the terminal 20, the processor 21 receives the display information from the work information processing device 10 via the communication device 22 and may display the display information (for example, at least one of the actual RE value AV and the comparison reference value SV) via the display device 25. The display information may also include other information different from the actual RE value AV and the comparison reference value SV.

[0077] The processor 11 may display the RE performance values ​​AV for each store in a identifiable manner. In Figure 9, the RE performance values ​​AV for each store are displayed in order from store A to store G as a bar graph. Note that the graph format may be other than a bar graph. The RE performance values ​​AV shown as a bar graph are an example of work efficiency performance information that indicates the actual value of work efficiency.

[0078] Furthermore, the processor 11 may display a comparative reference value SV along with the RE performance value AV for each store group. The processor 11 may display at least one of the following as the comparative reference value SV: the top store statistics value V1, the headquarters recommended value V2, and the store characteristic regression value V3. In Figure 9, all of the top store statistics value V1, the headquarters recommended value V2, and the store characteristic regression value V3 are displayed, but it is sufficient to display at least one of the top store statistics value V1, the headquarters recommended value V2, and the store characteristic regression value V3. Also, the comparative reference value SV does not have to be displayed.

[0079] By displaying the RE performance value AV along with the comparative benchmark value SV, anyone reviewing the display can easily determine the status of each store's RE performance value AV relative to the comparative benchmark value SV, for example, whether it has already been achieved, not yet achieved, or to what extent it is lacking.

[0080] Furthermore, the processor 11 may add additional information to the RE performance values ​​AV of each store that exceed the comparison benchmark value SV. This additional information may be, for example, a star mark SM. The processor 11 may also color-code the line showing the comparison benchmark value SV. That is, the processor 11 may color-code the line showing the top store statistics value V1, the line showing the headquarters recommended value V2, and the line showing the store characteristic regression value V3. Color coding is just one example of how to realize different display modes. The processor 11 may also change the additional information according to the difference between the comparison benchmark value SV and each RE performance value AV, for example, by changing the number of star marks SM. For example, if the first RE performance value AV1 is significantly higher than the comparison benchmark value SV, and the second RE performance value AV2 is slightly higher than the same comparison benchmark value SV, more star marks SM may be added to the first RE performance value AV1 than to the second RE performance value AV2. Alternatively, the processor 11 may determine the number of stars based on such inter-store comparisons, or it may use the number of comparison criteria SV (top store statistics V1, headquarters recommended value V2, and store characteristic regression value V3) that the RE performance value AV exceeds as the number of stars.

[0081] Furthermore, the processor 11 may display the RE performance AV of only some stores within the store group G1, rather than displaying the RE performance AV of all the stores being compared. For example, it may display only the RE performance AV of N stores that have obtained RE performance AV close to that of the own store (e.g., point C). This allows the work information processing device 10 to reduce the number of display elements (the number of stores whose RE performance AV is displayed) and improve visibility.

[0082] When the work information processing system 5 designates its own store, the processor 21 of the terminal 20 may accept input of its own store (identification information of its own store) from the user (for example, the store manager of each store) via the operation device 24, and transmit the information of its own store to the work information processing device 10 via the communication device 12. The processor 11 of the work information processing device 10 may then receive the information of its own store via the communication device 12 and designate it as its own store.

[0083] Furthermore, the processor 11 may display the RE performance value for each store, for example, the most recent RE performance value (for example, the RE performance value for this fiscal year), or it may display past RE performance values ​​(for example, the RE performance value for last fiscal year). Also, for each operation, the processor 11 may not display the RE performance value for all stores being compared, but may display only the RE performance value for the top N stores or the top N% of stores with the largest RE performance values.

[0084] Furthermore, the processor 11 may display the RE performance value AV of its own store (e.g., point C) in a different display manner than that of the RE performance value AV of other stores. For example, the processor 11 may display the bar graph showing the RE performance value AV of its own store with wider bars than the bar graphs showing the RE performance value AV of other stores. The processor 11 may also highlight the outer contour of the bar graph showing the RE performance value AV of its own store by outlining it. The processor 11 may also display a mark (store identification mark) near the bar graph showing the RE performance value AV of its own store to indicate that it is information of its own store. In this way, by making the display manner of the RE performance value AV of its own store different from that of other stores, the work information processing device 10 can easily recognize the RE performance value of its own store, which is of high interest to the person checking the display (e.g., the store manager of each store).

[0085] Furthermore, the processor 11 may display a specific store other than its own store in the same display manner as the own store. In this case, the processor 21 of the terminal 20 may receive input for a specific store from a user (e.g., a manager at headquarters) via the operation device 24 and transmit information about the specific store to the work information processing device 10 via the communication device 12. The processor 11 of the work information processing device 10 may then receive the information about the specific store via the communication device 12 and designate it as the specific store.

[0086] Furthermore, the processor 11 may display only the RE performance value AV for its own store or a specific store, and the comparison reference value SV, and may not display the RE performance value AV for stores other than its own or a specific store.

[0087] Furthermore, the processor 11 may further divide one store group into multiple groups. Figure 10 shows an example of subdivided store groups and comparison criteria values ​​for each subdivided group. For example, the processor 11 may divide one store group into subdivided groups with high RE performance (high RE group), subdivided groups with medium RE performance (medium RE group), and subdivided groups with low RE performance (low RE group). The processor 11 may then derive comparison criteria values ​​for each subdivided group within the store group. The comparison criteria values ​​for each subdivided group may be derived in the same way as the comparison criteria values ​​for each store group described above, by treating each subdivided group in the same way as a single store group.

[0088] Even within the same store group, there can be significant variations in the RE performance values ​​of individual stores. For example, in Figure 10, it is considered unrealistic for store M to aim for the comparison benchmark value SVH of the high RE group. In response to this, the processor 11 may generate further subdivided groups within the same store group according to the RE performance values, and determine the comparison benchmark value SV for each subdivided group. In Figure 10, the comparison benchmark value SVH may be determined for the high RE group, the comparison benchmark value SVM for the medium RE group, and the comparison benchmark value SVL for the low RE group. Since point M belongs to the low RE group, the comparison benchmark value SVL is determined and displayed. This allows the work information processing device 10 to encourage each store to set a realistic RE value target, thereby preventing a decline in employee motivation.

[0089] The work information processing device 10 displays the information as shown in Figures 9 and 10, thereby allowing for easy comparison and verification of RE performance values ​​for multiple stores belonging to the same store group or the same subdivided group. Furthermore, the work information processing device 10 allows for easy verification of the achievement of each store's RE performance value against the comparison benchmark value SV through additional information. Additionally, the work information processing device 10 can easily distinguish the RE performance values ​​of its own store or a specific store from those of other stores, improving the visibility of information related to its own store or a specific store.

[0090] Next, we will explain an example of determining RE target values.

[0091] The processor 11 determines the RE target value for each task and for each store group. In this case, the processor 11 may determine the RE target value to be one of the comparison reference values ​​SV, namely the top store statistics value V1, the headquarters recommended value V2, and the store characteristic regression value V3. The processor 11 stores the determined RE target value in memory 13.

[0092] For example, after the comparison reference value SV is displayed on the display device 25, or after the display of the comparison reference value SV has started, the processor 11 may designate one of the comparison reference values ​​SV input via the operation device 24 as the RE target value. In this case, the processor 21 of the terminal 20 may receive input of one of the comparison reference values ​​SV from a user (e.g., a manager at headquarters or a store manager) via the operation device 24 and transmit information of one of the comparison reference values ​​SV to the work information processing device 10 via the communication device 12. The processor 11 of the work information processing device 10 then receives information of one of the comparison reference values ​​SV via the communication device 12 and may determine one of the comparison reference values ​​SV as the RE target value. In other words, for example, if a manager at headquarters or the like confirms each comparison reference value SV and aims for a predetermined comparison reference value SV by comparing it with the current RE actual value, the work information processing device 10 can determine this predetermined comparison reference value SV as the target RE actual value in response to user operation by the manager at headquarters or the like.

[0093] Furthermore, the processor 11 may determine any value input via the operating device 24 as the RE target value. Alternatively, the processor 11 may determine the highest value among the top store statistics V1, headquarters recommended value V2, and store characteristic regression value V3 as the RE target value, or it may automatically determine any of the top store statistics V1, headquarters recommended value V2, and store characteristic regression value V3 as the RE target value without user input.

[0094] Next, we will explain examples of generating work schedules and operational plans for each store. Figure 11 is a diagram illustrating the generation of work schedules and task plans.

[0095] The processor 11 obtains information on the expected workload (for example, the planned workload for a predetermined period) from memory 13, etc. In this case, for example, the processor 11 obtains instruction information for each task (for example, shipping information I4, picking instruction information I5) from memory 13. As an example, referring to shipping information I4 and picking instruction information I5, it is found that 10 items need to be picked during the picking time slot (for example, 10:00-12:00) for the picking (fresh produce) task (task ID: W0001) of the goods to be packed in the packing task (task ID: W0003). Therefore, the processor 11 can estimate that the workload required for the picking (fresh produce) task during this time slot is equivalent to picking 10 items. The processor 11 also obtains the RE target value from memory 13, etc. Such expected workloads may be derived for each time slot, further derived for each task, and further derived for each store.

[0096] For each task, the processor 11 calculates the man-hours required for that task based on the expected workload and the RE target value, and stores the information on the required man-hours in the memory 13. For example, if the RE target value is to pick 5 items per man-hour (1 person per hour), the required man-hours for picking (fresh produce) work during a time period (e.g., 10:00-12:00) are calculated to be 10 / 5 = 2 man-hours. Such required man-hours may be derived for each time period, further for each task, and further for each store.

[0097] The processor 11 obtains the working conditions of each worker from memory 13, etc. The working conditions include, for example, the conditions under which each worker can work, and include information such as available working days, available working hours, and available tasks. Based on each worker's working conditions and required man-hours, the processor 11 generates a work plan for each worker's work and a work plan for each task performed by each worker. For example, the processor 11 determines that workers HA and HB, whose working conditions are suitable, are to be used for picking (fresh produce) work during a specific time period (e.g., 10:00-12:00). Therefore, as an example, the processor 11 generates a work plan in which workers HA and HB work during a specific time period (e.g., 10:00-12:00) and a work plan in which workers HA and HB perform picking (fresh produce) work during that time period (e.g., 10:00-12:00). Such work schedules and task schedules may be derived for each time slot, further for each task, and further for each store.

[0098] According to the work information processing system 5 of this embodiment, each store can be classified into groups taking into account the conditions of each store, and the work efficiency of stores belonging to the same store group can be easily compared. For example, the work information processing system 5 can produce actual values ​​for store groups with the same store conditions that affect the RE value for each task, and display the actual RE values ​​of each store for each store group side by side.

[0099] Even for the same task, such as picking for online supermarkets, there is variation in RE (Revenue Average) performance between stores. In this case, it is difficult for managers at headquarters to objectively determine what level of RE target value should be aimed for when improving work processes, and it is difficult to set comparative benchmarks and RE targets.

[0100] Furthermore, the size of the sales floor and work area in each store where the work is performed varies from store to store. For example, if the sales floor or work area is small, the time required for picking work is short. On the other hand, if the sales floor or work area is large, the distance workers have to travel is long and there are many products, so the time required for picking work is long. Therefore, the standard RE value may differ depending on the situation (store characteristics) of each store. In other words, even if you line up the RE performance values ​​for picking work (fresh produce) for all stores and try to aim for the RE performance value that represents the best work efficiency, it may not be an appropriate comparison standard value or RE target value for each store because the characteristics of each store are different.

[0101] In response, the work information processing system 5 determines store classification criteria according to the work characteristics and designates stores with similar store characteristics as comparable stores into the same store group. The work information processing system 5 then displays the RE performance values ​​of multiple stores belonging to this same store group side by side. In this case, the work information processing system 5 can recognize, for example, that even with the same store characteristics, there are stores with high RE performance values ​​and stores with low RE performance values. Furthermore, the work information processing system 5 can indicate the quality of each store's RE performance value by comparing it with comparison criteria values ​​such as statistical values ​​within the same store group. In addition, since the work information processing system 5 can visualize work performance (e.g., RE performance values) by grouping stores under the same conditions, it is possible to easily compare work performance between stores for each task. Moreover, the work information processing system 5 can set RE target values ​​corresponding to objective comparison criteria values ​​for each task and can also formulate work plans and work plans to achieve these targets.

[0102] In this embodiment, the work information processing system 5 has been described as processing information related to work at the online supermarket, but it is not limited to this. For example, this embodiment can be applied to any work where the RE value depends on store characteristics (e.g., store layout, store equipment, amount of work at the store, store's trading area, average skill level of workers at the store).

[0103] Figure 12 shows an example of store characteristics related to cashier operations. In Figure 12, cashier operations may include, for example, manned cashier operations, semi-self-checkout assistance, settlement operations, change exchange operations, and cashier opening and closing operations. Store characteristics may include, for example, the average number of items purchased per customer, the cash payment ratio, and the longest distance between target cashiers. Also, in Figure 12, as in Figure 6, the store classification criteria are indicated by circles. For example, in manned cashier operations, the average number of items purchased per customer and the cash payment ratio are shown as criteria for determining the store classification.

[0104] Figure 13 shows an example of store characteristics related to the manufacturing process of conveyor belt sushi. In Figure 13, The manufacturing process for conveyor belt sushi may include material preparation, preliminary preparation and cutting, rice preparation, nigiri (sushi sushi making), and maki (rolling) operations. Store characteristics may also include the version of the rice machine installed (sometimes simply abbreviated as "ver"), the types of sushi offered, and the size of the workspace. In Figure 13, as in Figure 6, the store classification criteria are indicated by circles. For example, in the nigiri operation, the types of sushi offered at the store and the size of the workspace at the store are shown as criteria for determining the store classification.

[0105] Furthermore, while this embodiment exemplifies that the terminal 20 handles the reception of input operations and the display of various information, it is not limited to this. For example, the work information processing device 10 may have the functions of the terminal 20 and may include an operation device and a display device. In this case, the operation device of the work information processing device 10 may accept various input operations. The display device of the work information processing device 10 may display the RE actual value and comparison reference value derived by the work information processing device 10.

[0106] Although various embodiments have been described above with reference to the drawings, it goes without saying that this disclosure is not limited to such examples. It is clear to those skilled in the art that various modifications or alterations can be conceived within the scope of the claims, and these will naturally also fall within the technical scope of this disclosure. Furthermore, the components of the above embodiments may be combined in any way without departing from the spirit of the invention.

[0107] In the above embodiment, the processor may be physically configured in any way. Furthermore, by using a programmable processor, the processing content can be changed by changing the program, thus increasing the design flexibility of the processor. The processor may consist of a single semiconductor chip, or it may be physically composed of multiple semiconductor chips. When composed of multiple semiconductor chips, each control of the above embodiment may be realized by a separate semiconductor chip. In this case, it can be considered that these multiple semiconductor chips constitute a single processor. The processor may also consist of a semiconductor chip and components having other functions (such as capacitors). Furthermore, a single semiconductor chip may be configured to realize both the functions of the processor and other functions. Furthermore, multiple processors may be composed of a single semiconductor chip.

[0108] The execution order of operations, procedures, steps, and stages in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before" or "prior to," and can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," "next," etc., for convenience, this does not mean that it is essential to perform the operations in that order.

[0109] (Summary of the embodiment) As described above, the work information processing device 10 of the above embodiment processes information related to work performed in stores. The work information processing device 10 includes a processor 11. For each task, the processor 11 acquires the actual work efficiency value (e.g., RE actual value) for the work in multiple stores for each store. For each task, the processor 11 determines a reference store characteristic, which is a store characteristic that serves as a basis for classifying the multiple stores into multiple store groups, from among multiple store characteristics that represent the characteristics of each of the multiple stores. For each task, the processor 11 classifies the multiple stores into multiple store groups based on the reference store characteristic. For each task and for each store group, the processor 11 displays the actual work efficiency information, which shows the actual work efficiency value of each store, on the display device 25 in a way that allows each store to be identified. In this case, the processor 11 may transmit the actual work efficiency information to the terminal 20 via the communication device 12. The terminal 20 may receive the actual work efficiency information from the processor 21 via the communication device 22 and display the actual work efficiency information via the display device 25.

[0110] As a result, the work information processing device 10 can classify multiple stores to be compared into multiple store groups by determining a standard store characteristic from among multiple store characteristics for each task, and by taking into account characteristic store characteristics that may affect each task. Therefore, the work information processing device 10 can visualize the work efficiency between stores with similar store characteristics and make it easier to compare the work performance of each store by displaying the work efficiency performance information in an identifiable way for each store. In this way, the work information processing device 10 can classify each store into groups taking into account the situation of each store, and easily compare the work performance of stores belonging to the same group.

[0111] Furthermore, the processor 11 may specify identification information for the first store and display the work efficiency performance information for the first store identified by the identification information for the first store in a different display manner than the work efficiency performance information for other stores.

[0112] This makes it easier for verifiers who check the display of work efficiency performance information (for example, the manager of the first store or a manager at headquarters who wants to check the information of a specific store) to identify the first store by comparing it with the work efficiency performance information of other stores, and facilitates various comparative analyses based on the first store.

[0113] Furthermore, the processor 11 may determine the reference store characteristics by acquiring the reference store characteristics input via the operation device 24.

[0114] This allows managers and others who understand the store characteristics that contribute to work efficiency to use the operating device 24 to specify the desired store characteristics as the standard store characteristics. Therefore, it becomes possible to classify store groups in a way that reflects the intentions of managers and others.

[0115] Furthermore, the processor 11 may calculate correlation information for each store characteristic, showing the correlation between the actual work efficiency and the store characteristic for each task. The processor 11 may determine a standard store characteristic for each store characteristic based on the correlation information for each store characteristic for each task.

[0116] As a result, even when it is difficult to identify the store characteristics that should be used as the standard store characteristics, the work information processing device 10 can determine the standard store characteristics through correlation analysis based on past performance. Therefore, the work information processing device 10 can use store characteristics that are likely to affect work efficiency as the standard store characteristics.

[0117] Furthermore, for each task, the processor 11 may derive the importance of each of the multiple store characteristics based on a regression model that derives work efficiency from the multiple store characteristics. For each task, the processor 11 may determine a reference store characteristic based on the importance of each of the multiple store characteristics.

[0118] As a result, even when it is difficult to determine the store characteristics that should be used as the standard store characteristics, the work information processing device 10 can determine the standard store characteristics using a regression model, for example, by training a regression model based on past performance. Therefore, the work information processing device 10 can use store characteristics that are likely to affect work efficiency as the standard store characteristics.

[0119] Furthermore, store characteristics may include at least one of the following: the size of the store's sales floor, the degree of congestion in the store's sales floor, the method of packing work included in the store's operations, the size of the workspace where the store's operations are performed, the number of orders for the products that the store's operations are performed on, the equipment used to perform the store's operations, the layout of the store's equipment, the average skill level of the workers performing the store's operations, the volume of work performed in the store, and the trading area in which the store is located.

[0120] This allows the work information processing device 10 to determine the most suitable store characteristics from among various store characteristics as the standard store characteristics.

[0121] Furthermore, the standard store characteristics can be those that contribute to work efficiency.

[0122] This allows the work information processing device 10 to determine store characteristics that are likely to affect work efficiency as standard store characteristics. Therefore, by classifying stores with the same or similar store characteristics into the same store group, it becomes possible to compare the work efficiency of stores that can achieve similar work efficiency.

[0123] Furthermore, the processor 11 may display, for each task and for each store group, actual work efficiency information and a comparison standard value for comparing with the actual work efficiency value of each store.

[0124] This allows those reviewing the display to easily compare and verify the operational efficiency performance information of each store against the comparison benchmark.

[0125] Furthermore, the processor 11 may acquire comparison reference values ​​input via the operating device for each operation and for each store group.

[0126] This allows, for example, a manager at headquarters to specify recommended values ​​for a given store group as comparison benchmark values ​​through user input.

[0127] Furthermore, the processor 11 may determine a statistical value of the actual work efficiency of each store as a comparison standard for each task and for each store group.

[0128] This allows the work information processing device 10 to determine a comparative standard value based on a statistical analysis of the actual work efficiency values ​​of each store.

[0129] Furthermore, the processor 11 may calculate an appropriate value for work efficiency for each task and each store based on a regression model that derives work efficiency from multiple store characteristics, and determine the appropriate value for work efficiency for each task and each store as a comparison standard value.

[0130] As a result, the work information processing device 10 can determine a reasonable work efficiency as a comparison standard value, for example, by taking into account the characteristics of a standard store in the store group, based on a regression model that is based on past performance.

[0131] Furthermore, for each task and each store group, the processor 11 may determine one of the following as the target value for work efficiency: a comparison reference value input via an operating device, a comparison reference value as a statistical value of the actual work efficiency of each store, and a comparison reference value as an appropriate value of work efficiency calculated based on a regression model that derives work efficiency from the characteristics of multiple stores. For each task and each store, the processor 11 may generate a work plan for performing the task or a work schedule for the worker performing the task based on the target value for work efficiency.

[0132] As a result, the work information processing device 10 can formulate a work plan or work schedule that can achieve the work efficiency that will reach the determined target value of work efficiency.

[0133] Furthermore, the processor may specify a target value for work efficiency by displaying work efficiency performance information and comparison benchmark values ​​for each task and each store group, and then obtaining the comparison benchmark value to be used as the target value for work efficiency entered via the operating device.

[0134] This allows the person reviewing the display to check and consider the actual work efficiency information and the benchmark values, and then specify the target value for work efficiency within the store group through user input. [Industrial applicability]

[0135] This disclosure is useful for work information processing equipment, work information processing method, and programs that can easily compare work performance between stores belonging to the same group by classifying stores into groups based on the circumstances of each store. [Explanation of symbols]

[0136] 5. Work Information Processing System 10. Work Information Processing Device 11 processors 12 Communication devices 13 memory 20 devices 21 processors 22 Communication devices 23 memory 24 Operating Devices 25 Display Devices AV RE actual value SV, SVH, SVM, SVL Comparison Reference Values SM Star Mark V1 Top Store Statistics V2 Headquarters Recommended Values V3 Store characteristic regression value

Claims

1. A work information processing device that processes information related to work performed in a store, Equipped with a processor, The aforementioned processor, For each of the aforementioned tasks, the actual work efficiency values ​​for the aforementioned tasks at multiple stores are obtained for each store. For each of the above operations, a standard store characteristic is determined from among the multiple store characteristics that represent the characteristics of each of the multiple stores, which is a store characteristic that serves as the basis for classifying the multiple stores into multiple store groups. For each of the above operations, the multiple stores are classified into the multiple store groups based on the categories or specific values ​​included in the determined standard store characteristics. For each of the aforementioned tasks and for each of the aforementioned store groups, work efficiency performance information showing the actual work efficiency of each store is displayed on a display device in a way that allows each store to be identified. In determining the aforementioned standard store characteristics, (i) For each of the above tasks, a correlation coefficient is calculated that shows the correlation between the actual work efficiency and the characteristics of each store, and the store characteristics for which the absolute value of the correlation coefficient is equal to or greater than a predetermined threshold are determined as the standard store characteristics. Or, (ii) For each of the above operations, a regression model is trained to derive the work efficiency from the multiple store characteristics, and based on the importance of each store characteristic, store characteristics whose importance is above a predetermined threshold or a predetermined number of store characteristics corresponding to the highest importance are determined as the standard store characteristics. Work information processing device.

2. The aforementioned processor, Obtain the identification information of the first store, The work efficiency performance information of the first store, identified by the identification information of the first store, is displayed in a different display manner from the work efficiency performance information of other stores. The work information processing device according to claim 1.

3. The store characteristics include at least one of the following: the size of the store's sales floor, the degree of congestion in the store's sales floor, the method of packing work included in the work at the store, the size of the workspace where the work is performed at the store, the number of orders for the products that are the subject of the work at the store, the equipment for performing the work at the store, the layout of the equipment at the store, the average skill level of the workers performing the work at the store, the amount of work performed at the store, and the trading area in which the store is located. The work information processing device according to claim 1 or 2.

4. The aforementioned standard store characteristics are store characteristics that contribute to the aforementioned work efficiency. The work information processing device according to claim 1 or 2.

5. The aforementioned processor, For each of the aforementioned tasks and for each of the aforementioned store groups, the actual work efficiency information and a comparison standard value for comparing it with the actual work efficiency value of each store are displayed. The work information processing device according to claim 1 or 2.

6. The processor acquires the comparison reference value input via the operating device for each operation and each store group. The work information processing device according to claim 5.

7. The processor determines, for each task and for each store group, a statistical value of the actual work efficiency of each store as the comparison reference value. The work information processing device according to claim 5.

8. The aforementioned processor, For each of the aforementioned tasks and each of the aforementioned stores, an appropriate value for the work efficiency is calculated based on a regression model that derives the work efficiency from the characteristics of the multiple stores. For each of the aforementioned tasks and each of the aforementioned stores, the appropriate value for the aforementioned work efficiency is determined as the aforementioned comparison standard value. The work information processing device according to claim 5.

9. The aforementioned processor, For each of the aforementioned tasks and for each of the aforementioned store groups, one of the following is determined as the target value for work efficiency: the comparison reference value input via the operating device, the comparison reference value as a statistical value of the actual work efficiency of each store, and the comparison reference value as an appropriate value of work efficiency calculated based on a regression model that derives the work efficiency from the characteristics of the multiple stores. For each of the aforementioned tasks and for each of the aforementioned stores, a work plan for performing the task or a work schedule for the worker performing the task is generated based on the target value of the aforementioned work efficiency. The work information processing device according to claim 5.

10. The processor, after displaying the work efficiency performance information and the comparison reference value for each work and each store group, selects the target value for work efficiency by obtaining the comparison reference value, which is the target value for work efficiency, input via the operating device. The work information processing device according to claim 9.

11. A work information processing method for processing information relating to work performed in a store using a work information processing device equipped with a processor, The processor obtains, for each operation, the actual value of the work efficiency of the operation at multiple stores for each store. The processor, for each operation, determines a reference store characteristic, which is a store characteristic that serves as a criterion for classifying the multiple stores into multiple store groups, from among multiple store characteristics that represent the characteristics of each of the multiple stores. The processor, for each operation, classifies the plurality of stores into the plurality of store groups based on the categories or specific values ​​included in the determined standard store characteristics, The processor provides, for each task and for each store group, work efficiency performance information indicating the actual work efficiency of each store, which is displayed on a display device in a way that allows each store to be identified. It has, The step of determining the aforementioned standard store characteristics is: (i) For each of the above tasks, calculate a correlation coefficient showing the correlation between the actual value of the task efficiency and the characteristics of each store, and determine the store characteristics for which the absolute value of the correlation coefficient is equal to or greater than a predetermined threshold as the standard store characteristics. Or, (ii) For each of the above operations, a regression model is trained to derive the above work efficiency from the multiple store characteristics, and based on the importance of each store characteristic, store characteristics whose importance is above a predetermined threshold or store characteristics corresponding to a predetermined number of importance levels from the top are determined as the standard store characteristics, Work information processing method.

12. A program for causing a computer to execute each step of the work information processing method described in claim 11.

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