JP5926819B2 - Store operation information system and business system operation information system - Google Patents

Description

  The present invention relates to an operation information system for a store or business system, and in particular, a store manager who is a manager of a chain store, etc., is required to operate his store or business system (hereinafter referred to as a store). The present invention relates to a management information system for supporting the grasp of the current state of an employee, etc., and for assisting the creation of work schedules and business instructions for employees working in stores based on the present state grasp.

  One activity that seeks to maximize productivity at retail sites is an activity called In Store Merchandizing (ISM). In this ISM, a formula representing a purchase unit price (customer unit price) per customer at a store is often expressed as the following formula (10).

Customer unit price = Number of units purchased (= Line length x Stop rate x Purchase rate) x Product unit price (10)
In other words, it has been pointed out that in order to improve the unit price of customers, it is necessary to grasp the flow line length, the stop-by rate, the purchase rate, the number of items purchased, and the product unit price, and take measures against each. Among these, “Flow line length” indicates the product area classification and product shelf layout, “Stop by rate” and “Purchase rate” indicate product placement on the product shelf, and POP (Point of Purchase) It is considered that the provision of information by means of advertising) is the key to “product unit price” appealing for higher-priced products. In this way, the ISM shows the importance of grasping the current status of flow line length, stopover rate, purchase rate, number of items purchased, and unit price of goods, and measures based on it to improve productivity at retail sites. . In any case, it goes without saying that accurate understanding of customer behavior in the store is the key to determine the success or failure of such management.

  Of the indicators to be grasped in ISM, ie, the flow line length, stopover rate, purchase rate, quantity purchased, and unit price of goods, for example, the flow line length is measured by mounting a sensor in the shopping cart to visualize the customer flow line. Or a statistical analysis, or a technique for analyzing a stay time, a flow line length, and an average moving speed for each product area.

  In addition, for the measurement of the stop-off rate and purchase rate, for example, it is possible to detect a stop at a fitting store in a clothing store with a sensor, detect a stop based on a camera image of a convenience store, etc. A technique for analyzing a purchase rate together with a POS (Point of Sales) system is disclosed. Needless to say, information on the number of items purchased and the product unit price can be obtained from the POS system.

  In addition to these customer-focused technologies, as a technology focused on employee in-store behavior, employee work proficiency based on voice, etc., correlation between customer satisfaction and store satisfaction, and sales A technique for performing the correlation calculation is also disclosed. In addition, a technique for identifying the worker's engagement content based on data measured by a name tag type sensor, a wristwatch type sensor, a use log of a mobile phone, or other means is also available. For the purpose of improving store sales in this way, it is a well-known technology to analyze human behavior information such as customer behavior and employee behavior in combination with POS system information such as sales. Are known.

  In addition, regarding the schedule creation of employees, the “schedule table creation device” in Patent Document 1 creates a shift table based on the business volume for each time zone and the business volume prediction model, and the sensor attached to each store clerk. A technique for creating a shift table using information and POS system data is disclosed. As a technology to support store operations in this way, the sensor burden is used to automatically create employee schedules (shift table creation) using the IT system. Can be reduced.

JP 2009-37568

  Store managers and employees (store clerks) at stores have general know-how and knowledge about what kind of in-store promotions can increase sales and what kind of product layout and shelf allocation can increase sales. However, there is a problem that it tends to be a final judgment based on each person's experience, skills, and intuition. Therefore, it is possible to manage the store manager by using the IT system to visualize or recommend (recommend) based on POS data and human behavior data by attaching sensors to measure the behavior of customers and employees. It is thought that this will contribute to improving the productivity of the store. In addition, in order to support the office work of busy store managers, the creation of employee schedules (creation of shift tables) and the creation of employee work instructions (creation of work instructions), which generally tend to increase the amount of paperwork ) Automatically, it is possible to reduce the administrative burden on the store manager.

  In the shop floor, it is always required to secure the profit of the shop by improving the business efficiency. However, store managers operate stores based on intuition and experience, so it seems that store managers have become increasingly busy in recent years. The group of technologies described in the background art is the development of a wide range of technology to shift from conventional store operations based on intuition and experience to more highly accurate store operations based on analysis of data collected from sensors and POS systems. It is an example. However, while there are many stores where POS systems are installed at cash registers, efforts to periodically attach sensors to customers and shop assistants have just begun. For this reason, we often see news that new products tailored to customer preferences have been developed using POS data, but there are few examples of using stores to streamline store operations.

  One of the reasons that the introduction of sensors in actual stores does not progress as compared with the POS system is that psychological resistance to customers and employees wearing sensors. However, it is clear that (1) what kind of data is collected by sensors and (2) what kind of information provision and business support are performed as IT systems can make store operations more efficient. It seems that there is nothing. In particular, the purpose of acquiring employee behavior instead of customer behavior using sensors at stores is mainly for the purpose of improving operational efficiency rather than increasing sales, in order to improve store productivity. It is thought to be done.

  However, what is required of store managers is “to secure profits at their own stores” as a result, and it is desirable to introduce and utilize sensors in actual stores in a way that directly leads to improved sales at their own stores.

  An object of the present invention is to provide an operation information system for a store or business system that obtains employee behavior and proposes an operation improvement with a high probability of directly leading to an increase in sales of the store based on the analysis of the collected data and the like There is to do.

  One representative example of the present invention is as follows. Multiple types of store support functions, which are a plurality of indicators indicating the operation state of a store based on a function given in advance, with information indicating an employee's behavior changing with time in a predetermined real space inside the store A store support function calculation unit that calculates the pattern, a pattern determination unit that determines which type of the situation pattern matches the output of the store support function, each calculated output value of the plurality of types of store support functions, and the A situation pattern storage unit that records situation patterns as time series data, a pass / fail correlation information storage unit that records a plurality of preset pass / fail correlation determination patterns, a shift pattern for each employee, and the work status thereof A shift pattern storage unit that records time-series data, and a productivity improvement information generation unit, wherein the plurality of types of store support functions are the working status of the employee, Work contents of work personnel, location arrangement, time distribution, data representing motivation and vitality, and data representing the sales situation of the store, of the plurality of types of store support functions calculated in the store support function calculation unit The situation pattern determination unit determines which combination matches the type of the situation pattern, and the productivity improvement information generation unit converts the time pattern data of the situation pattern into the pass / fail correlation determination pattern. Extracting a corresponding typical pattern, determining whether or not there is a time-series correlation between the typical pattern and the work situation for each employee, and generating information related to operational improvement of the store using the time-series correlation And

  According to the present invention, the manager generates operation information that can be comprehensively evaluated the current state of the store, etc., which is generated from information relating the employee behavior and the sales improvement of the store, etc., and provides the manager with the management information. You can increase opportunities to be directly involved in improving the management of your store.

It is a figure explaining the whole structure and function of the store management support information system based on one Example of this invention. It is a figure for the store information system of this invention to overlook the kind of data which should be collected in a store. It is a figure which shows four types of data which the store information system of this invention should collect in a store. It is a figure which shows an example of the definition of the time allocation in FIG. 3A. It is a figure which shows the example of the table which shows the pattern type of the situation of an employee in the store information system of this invention. The store information system of this invention is an example of utilization when collecting data using a sensor in a real store. It is a figure which shows the specific system configuration example of the shop information system which concerns on the Example of this invention. It is a figure which shows the relationship between a data group and a calculation program in the shop information system of a present Example. It is a figure which shows an example of the quality correlation determination pattern of the time-system change of a situation pattern. It is a figure which shows an example of the factor pattern of improvement and a fall. It is a figure which shows an example of the flowchart which performs the process of location arrangement optimization in the shop information system of a present Example. It is a figure which shows the screen which displayed the example of the situation pattern information in the shop information system of a present Example. It is a figure which shows the screen which displayed an example of the time series data of the situation pattern in the shop information system of a present Example. It is a figure which shows the screen which displayed the shop comparison of the situation pattern in the shop information system of a present Example. It is a figure which shows the screen which displayed the extraction example of the typical pattern. It is a figure which shows an example of the flowchart which calculates | requires an extraction typical pattern and the time series correlation information of an employee. It is a figure which shows an example of the flowchart which produces | generates the improved shift pattern based on an extraction typical pattern and a factor. It is a figure which shows the work schedule (next day recommendation) screen classified by time zone produced | generated by the process of FIG. It is a figure which shows the system of the business instruction document in the store information system of a present Example.

  The present invention collects data by using sensors worn by customers and employees in stores and business systems (hereinafter simply referred to as stores), and can provide store managers with useful information for store operations. Regarding information systems. The invention “store management support information system” in the present specification may be abbreviated as “the present system” hereinafter.

This system uses employee sensors to calculate (1) location, (2) time allocation, (3) motivation and vitality, and (4) work efficiency for each employee and work content. First to fourth data are acquired. On the other hand, the numerical processing functions (store support functions) F1, F2, F3, and F4 are defined corresponding to these four types of data. Subsequently, threshold values Th1, Th2, Th3, and Th4 are provided for each of the functions F1, F2, F3, and F4 in order to calculate a “pattern” obtained by combining these plural values. When the function F1 exceeds the threshold value Th1, “F1 = large”, and when the function F1 is equal to or less than the threshold value Th1, “F1 = small”. For example, the case of “F1 = large”, “F2 = large”, “F3 = large”, and “F4 = large” is defined as the situation pattern 1. The same applies thereafter. By categorizing the situation patterns in this way, the overall state of the store is combined with the combination of the location of the store clerk, the time distribution of the store clerk, the motivation / energy of the store clerk, and the work efficiency of the store clerk Can be expressed. In addition, comparisons between multiple stores are also made using situation patterns. Finally, a schedule table (shift table) and a business instruction sheet corresponding to the type of the situation pattern are output so that the store manager can view them. As a result, store managers are provided with information to shift from conventional store operation based on intuition and experience to store operations with higher accuracy based on analysis of data collected from sensors and POS systems. It is possible to reduce office work, especially office work, secure on-site stay time, and perform qualified store operations.
That is, the present invention discloses an invention that aggregates data collected by a sensor system and provides information to a manager such as a store manager or makes a recommendation (recommendation) in order to achieve introduction promotion of the sensor system to an actual store. According to the present invention, an IT system capable of supporting a store manager / manager of a store in order to appropriately give instructions to employees in order to achieve a profit quota is provided.
Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a diagram for explaining the overall configuration and functions of a store management support information system according to an embodiment of the present invention.

  In FIG. 1, reference numeral 100 denotes a server of the store A that is the main body of the store management support information generation system according to the present invention, 101 is an input processing unit, 102 is an output processing unit, and 107 is a communication processing unit. Furthermore, by executing a program on the server 100, various functions as described below are realized. That is, 103 is a store A function pattern / countermeasure definition unit based on a combination of store support functions, 104 is a store support function (F1-F4) calculation unit, 105 is a store A situation pattern determination unit using the store support function, Reference numeral 106 denotes a productivity improvement information generation unit. The store management support information generation system 100 further stores, as storage means, a database 110 that stores information on “store support functions” and a database that stores information on “store situation patterns / good / bad correlation determination patterns / countermeasure definition”. 111, database 112 holding “other store information”, database 113 holding “external factor” information, database 114 holding “time series data / good / bad correlation” information, “shift table / business improvement” A database 115 or the like for holding the above information. Reference numeral 120 denotes a store manager or a manager terminal equivalent to the store manager, and the display screen thereof has a user interface function and is configured to be able to communicate with the server 100 of the store A by wireless communication.

  Reference numeral 131 denotes a customer sensor associated with a customer at the store A, 132 an employee sensor associated with an employee at the store A, and 133 a POS system. As the shape of the sensor, a sensor of various shapes such as a name tag type sensor, a camera, an RFID, a shopping cart, or a shopping basket may be used. As the sensor type, any of various sensors for detecting infrared rays and acceleration, a device for detecting sound such as a microphone, and a device for acquiring an image such as a camera may be used. 140 is a diagram showing an outline of store support functions F1 to F4 generated by the store support function calculation unit 104 based on data collected by the sensors 131 and 132 and the POS system 133.

  The value output when the first data related to the “location / arrangement” is input to the store support function F1 is a calculated value based on spatial activity data representing the work situation of the store clerk. The value output when the second data related to the “time allocation” is input to the function F2 is a calculated value based on the time activity data of the store clerk. The value output by inputting the third data related to “motivation / energy” to the function F3 is a calculated value based on the internal activity data of the store clerk. The value output when the first data related to the “working efficiency” is input to the function F4 is a calculated value necessary for PDCA based on the relationship between the clerk activity and the store sales. Details of the store support functions F1 to F4 will be described later.

  Further, the server 100 of the store A is connected to the headquarter server 150 and the server 160 of another store via the communication network 170. The headquarters server 150 has a function 151 for collecting / managing / distributing each store information and a function 152 for setting “external factor” common to each store as functions realized by executing the program on the server. , A function 153 for setting “countermeasure information” and the like, and a related database.

  The server 100 of the store A collects various data in the store A, generates a store support function, analyzes the current status of the store A based on the current status pattern obtained from those values, and improves it. By visualizing the strategy and presenting it to the store manager, the store manager is supported by the busy business. By using the present invention, it is possible to particularly reduce the office work of the store manager / manager and to secure time for the store manager to stay at the site.

Next, what kind of data should be collected by the sensor in the store will be described.
FIG. 2 is a view for overlooking the types of data that the store information system of the present invention should collect in a store. Currently, there are a wide variety of tasks that must be covered by store managers, and it is necessary to make various decisions related to them instantaneously and appropriately. Therefore, the customer sensor 131, employee sensor 132, and POS system 133 are used to collect, analyze, and visualize p and q data as shown in FIG.

  The purpose of acquiring customer behavior in a store using a sensor and its related system (hereinafter referred to as a sensor system) is to perform analysis mainly for improving sales in the productivity improvement of the store. The upper half of FIG. 2 shows the relationship among the indicators (flow line length (p1), stopover rate (p2), purchase rate (p3), purchase quantity, product unit price (p4)) that should be grasped in ISM. Show. The number of purchases can be obtained by multiplying the elements of the flow line length (p1), the stop-by rate (p2), and the purchase rate (p3). A customer unit price is obtained by multiplying the number of purchased items by an element of the product unit price (p4). The sales amount (Rx) is obtained by integrating the customer unit price for a predetermined time.

  The customer sensor 131 calculates a flow line length (p1), a stopover rate (p2), and a purchase rate (p3) of the customer. Further, by combining the data of these customer sensors with the data of the POS system (commodity unit price (p4)), the sales (Rx) = (Σ (customer unit price)) is obtained from the relationship of the following equation (11).

Sales (Rx) / External factor = f (p1, p2, p3, p4) (11)
“External factors” mean factors outside the company, such as climate and economic trends, which affect the sales of each store, for example. Equation (11) means “sales excluding fluctuations due to external factors”.

  In addition, when it is difficult to attach a sensor to a customer in terms of cost or operation, the number of purchases (p1), the product unit price (p4), the unit price of customers, the sales amount (Rx) using only the POS system. = (Σ (customer unit price)) can be acquired and used as input data of the server 100.

  The employee sensor 132 is attached to the employee with the consent of the employee on the premise that it is necessary to better understand the behavior of the employee for the purpose of improving business efficiency. Among the managers' duties, the employee management work is considered to increase the burden for the number of employees. In supermarkets, home centers, discount shops and other large stores, the number of employees is large, and this tendency is particularly noticeable. As to what kind of data is to be acquired using the employee sensor, (1) “location / location” (q1), (2) “time allocation” (for each worker and work content shown in FIG. It is sufficient to acquire q2), (3) “Motivation / Vitality” (q3), and (4) “Working efficiency” (q4). The above (1) and (2) are spatial and temporal activity data that can be grasped from the appearance to the clerk, and the above (3) is internal activity data that cannot be grasped from the appearance. The above (4) grasps how store clerk's activities correlate with store sales, and as a whole PDCA (Plan → Do → Execution → Check → Act (Improvement) ) Data for rotating the cycle. Based on q1 to q4, sales (Rx) = (Σ (customer unit price)) is obtained from the relationship of the following equation (12).

Sales (Rx) / External factor = g (q1, q2, q3, q4) (12)
Here, the lower half of FIG. 2 shows an object to be measured by attaching a sensor to an employee along a general 5W1H system. In Fig. 2, the 5W1H of the employee's behavior is divided into the work staff (who), the work contents (what), the location (where (q1)), the time allocation (when (q2)), the motivation / energy (how (Q3)), and the purpose (why) of the action is to improve the sales of the store. However, since q1, q2, and q3 alone do not provide correlation with store sales (Rx), which is an objective numerical value, the concept of work efficiency (q4) is introduced, and the clerk's activities It helps to understand how it correlates with sales.

  In other words, Expression (11) and Expression (12) capture sales from the same store A at the same time from two different viewpoints. In the present invention, the relationship between the sales results and the store operation status is analyzed from the information of these p (p1, p2, p3, p4) and q (q1, q2, q3, q4), and fed back to the store operation.

  Therefore, in the present invention, the respective numerical processing functions F1, F2, F3, and F4 corresponding to the four types of data (1) to (4) acquired by the employee sensor 132 are defined. (1) A value (hereinafter referred to as F1 ((1) place arrangement)) output when the place arrangement data is input to F1 is a calculated value based on the spatial activity data of the store clerk. F2 ((2) time allocation) is a calculated value based on the time activity data of the store clerk. F3 ((3) Motivation / Vitality) is a calculated value based on the internal activity data of the store clerk. F4 ((4) work efficiency) is a calculated value necessary for PDCA based on the relationship between the salesclerk's activity and the sales of the store.

  In addition, F1 ((1) place arrangement), F2 ((2) time distribution), F3 ((3) motivation and vitality), and F4 ((4) work efficiency) show different aspects of the clerk's activities, respectively. Yes. For this reason, it is not appropriate to aggregate and display in a smaller number of functions and indicators. Therefore, it is not appropriate to represent a store clerk's activity as a single value or a single index, and it is more appropriate to represent the situation by a “situation pattern” in which a plurality of values are combined. Therefore, threshold values Th1, Th2, Th3, Th4 are set for F1, F2, F3, and F4, respectively. For example, by changing the threshold based on “external factor” in the server 150 of the headquarters, the expressions (11) and (12), in other words, “continuity” and “objectivity” of the “situation pattern” Is secured. For example, since there is a high probability that sales of all stores will generally increase during a booming economy, the threshold value of all stores is naturally larger than the threshold value during recession. Alternatively, the threshold may be changed due to circumstances specific to only a specific store, such as when a store of a competitor in the industry is opened near a certain store and competition intensifies.

  In the “situation pattern”, for example, when F1 ((1) place arrangement) exceeds the threshold value Th1, “F1 = large”, and when the threshold value is equal to or less than the threshold value Th1, “F1 = small” is expressed. And The same applies to F2 to F4. For example, the case of “F1 = large”, “F2 = large”, “F3 = large”, and “F4 = large” is defined as the situation pattern 1. Next, for example, the situation pattern 2 is defined as “F1 = large”, “F2 = large”, “F3 = large”, and “F4 = small”. The same applies thereafter. The advantages expressed in the situation pattern as described above are the overall condition of the store by the combination of the location of the store clerk, the time distribution of the store clerk, the motivation / activity of the store clerk, and the level of work efficiency of the store clerk. It can be expressed in It is considered that comparison between multiple stores can be performed relatively easily by using the situation pattern.

  The merit of displaying the state of the store in the situation pattern is not only improved in comparability. Another advantage is that a prescription corresponding to each situation pattern can be presented to the store manager. Specifically, the schedule table (shift table) and business instructions are automatically created by the IT system according to the prescription, which reduces the time required for employee management operations, which is a heavy workload for store managers. It becomes possible to connect.

  In this way, the actual data of (1) to (4) for each worker and work content shown in FIG. 2 are aggregated and numerically expressed by the above formulas (11) and (12), and objective by the situation pattern It is possible to support improvement of business efficiency in the PDCA of the store by making a determination and providing information to the manager such as the store manager or making a recommendation (recommendation). That is, according to the store management support information generation system according to the present invention, by presenting an objective determination result based on the analysis of actual data collected from a sensor or a POS system to a manager such as a store manager, the accuracy is further improved. A store information system for shifting to store operations can be provided to support busy operations such as store managers. In particular, it is possible to reduce office work and secure on-site stay time for store managers and the like, thereby supporting effective store management.

  In FIG. 2, an attempt was made to comprehensively describe the data elements necessary for store operation in accordance with the ISM theory system for customer data and in the 5W1H system for employee data. Other than the example shown in Fig. 2, it is thought that there are other factors necessary for store operation. However, the main purpose of store operation is to improve store productivity, particularly to improve both sales and operational efficiency. For example, in FIG. 2, it is considered that the elements necessary for store management are almost covered.

  For the four types of employee data (1) to (4) in the 5W1H system acquired by the employee sensor, the four functions F1, F2, F3, and F4 are used as the corresponding numerical processing functions. The definition used is an example, and it may be defined by three or more functions different from this.

  FIG. 3A is a diagram showing data related to (1) “place / arrangement”, (2) “time distribution”, (3) “motivation / energy”, and (4) “work efficiency” shown in FIG. It is. This is data obtained by measuring with a sensor or the like for a certain store. All of (1) to (4) are data with the horizontal axis representing elapsed time (t). As the data acquisition period, the basic unit from the opening time to the closing time of the store is acquired continuously over multiple days, or weekdays (Monday to Friday), weekends (Saturday and Sunday) or each Get data for only the day of the week.

  First, in FIG. 3A (1), the first data relating to the employee's “location / arrangement” indicates the relationship between the number of customers staying (bar graph) and the number of shop assistants (line graph) for each product area. It is data. The notation “Area 1” in the upper left square indicates that this plot is data in the product area 1 region. Therefore, in addition to area 1, there is similar data for each product area in the store, area 2, area 3,. The purpose of acquiring the first data related to this (1) “place / placement” is to increase the number of employees (store clerk) during the time when the number of staying customers increases. , Based on the hypothesis that it may encourage customer purchasing activities. On the other hand, even if a large number of employees (clerks) are arranged during a time period when the number of staying customers is small, it is considered that the work efficiency is poor, and it is desirable to engage the employees (clerks) for other work during that time period. The time distribution of this number of customers may vary depending on the day of the week, and it may be different at the turn of the season, whether there are competitors, whether there are events, even during long holidays such as Golden Week and New Year, If possible, it is desirable to obtain data every business day.

  Next, the second data relating to the “time allocation” of the employee in FIG. 3A (2) is data indicating the time allocation related to the work contents for each worker. The notation “store clerk A” in the upper left square indicates that this plot is data for clerk A, one of the employees. Therefore, in addition to store clerk A, the same data for each store employee (including store manager, deputy store manager, manager, employee, store clerk, part, part-time job, staff, etc.) such as store clerk B, store clerk C,. Exists. Alternatively, it is possible to obtain data of the entire salesclerk, which is a total of all salesclerks. “Front (responsible)” is the time period during which store clerk A is primarily engaged in store operations, and “front (non-responsible)” is that store clerk A is primarily engaged in store operations that are not in charge. During the time period, “front (unknown)” is the time when clerk A is engaged in store operations, but it is unclear whether the person in charge or not is in charge, “office work” means that clerk A is in the office, etc. “Backyard” is the time during which clerk A is engaged in inventory work in the backyard (the backside of the sales floor where the product inventory is stored and processing) The belt and the “checkout counter” are times when the clerk A is engaged in business at the cashier or service counter. The purpose of acquiring the second data related to this (2) “time allocation” is essentially non-existing, such as how much the store clerk is engaged in the work that should be in charge of, or the support of other store clerk. Identify how much you are engaged in the work you are responsible for and see if there is an uneven distribution of work, or whether the work volume is excessive or insufficient. This is to make use of the actual situation to create an optimal shift table. Similar to the first data, if the second data is also possible, it is desirable to obtain data for every business day.

  Subsequently, the third data related to the employee's “motivation / energy” in (3) of FIG. 3A is data indicating the energy / activity of each store in a plurality of stores. The notation “whole” in the upper left square indicates that this plot is data relating to the entire store clerk. The data for the entire store clerk is calculated by the sum of the measured values of motivation and vitality for each individual, but since the value for each individual is not important, the total amount of motivation and vitality for the entire store is important, so the display is It is enough to have only “whole”. The purpose of acquiring the third data related to this (3) “Motivation / Vitality” is that the store manager selects the store (A store here) and the other excellent store (B store here). This is because, by comparing the degree of motivation and vitality of employees (store clerk), the actual situation is utilized in the creation of business instructions. As with the first data, if the third data is also possible, it is desirable to obtain data for every business day.

  Finally, the fourth data relating to the “work efficiency” of the employee (4) in FIG. 3A is the work input amount for each product, product group or product area (for example, customer service time, stay time, shelf) Sales amount for each product, product group, or product area. There are various purposes for the work of employees, but it is thought that the goal is ultimately to improve the performance of the store (increase sales, lower costs, increase profits). In the fourth data of FIG. 3A, the sales amount is displayed as a representative as the business performance of the store. However, in addition to the sales amount, the cost amount and the profit amount may be displayed. The sales amount for each product can be extracted from the POS data. Sales information and cost information can be extracted from ERP (Enterprise Resource Planning). The notation “product group 1” in the upper left square indicates that this plot represents the sales amount of a product group. Therefore, in addition to the product group 1, similar data such as a product group 2, a product group 3,. It is also possible to acquire data that is a product that constitutes a product group, a product area that is composed of a plurality of product groups, or the total store for the entire store. The purpose of acquiring the data in (4) is to determine how much the amount of work input by employees is related to sales, so whether they are engaged in work that is not linked to sales increase. This is to clarify what work is related to work improvement and to make use of the actual situation in the creation of the optimal shift table and business instructions. As with the first data, if the fourth data is also possible, it is desirable to obtain data for every business day.

  Next, first to fourth data relating to (1) “place / arrangement”, (2) “time allocation”, (3) “motivation / energy”, and (4) “work efficiency” shown in FIG. 3A. F1, F2, F3, and F4 that are functions to be calculated will be described. In addition, it is possible to prompt the store manager's awareness and decision making by visualizing the first to fourth data as they are on the store manager's terminal with a graph or the like, but through the process of converting into a store support function From the viewpoint of making an appropriate decision in a short time, it is desirable to perform processing using the result value.

  First, the “place / placement” function F1 is a function for calculating the comparison between the number of customers and the number of employees with the first data in FIG. 3A as input, and is expressed by the following equation (1).

式（１）中のＣは顧客（Customer）人数、Ｓは店員（Shopper）人数を表す。ある商品エリアa、時間帯tにおけるＣをＣ_at、Ｓ_atと記す。|Ｃ_at−Ｓ_at|は、両者の差の絶対値である。関数Ｆ１は、この|Ｃ_at−Ｓ_at|を、全商品エリア、全時間帯で積分した値である。この場所配置の関数Ｆ１が大きいほど、商品エリアごと時間帯ごとの顧客人数に対する店員人数の差が大きいことを表しており、結果として顧客人数の変動に応じた適切な店員の場所配置ができていないことを示している。反対にこの関数Ｆ１が小さいほど、顧客人数の変動に応じた適切な店員の場所配置ができていることを示している。店長は、関数Ｆ１が小さくなるように、従業員（店員）の商品エリアごと時間帯ごとの配置を決めれば良い。本システムにおいて、改善策としてのシフト表や業務指示書の作成において、基本となる数値を提供するのが関数Ｆ１である。

In the formula (1), C represents the number of customers and S represents the number of shoppers. C in a product area a and time zone t is denoted as C _at and S _at . | C _at −S _at | is the absolute value of the difference between the two. The function F1 is a value obtained by integrating | C _at −S _at | in all product areas and all time zones. The larger the function F1 of the location arrangement, the larger the difference in the number of store clerks with respect to the number of customers for each product area and time zone. As a result, the location of appropriate store clerk according to the change in the number of customers is achieved. It shows no. On the other hand, the smaller the function F1, the better the location of the store clerk according to the change in the number of customers. The store manager may determine the arrangement for each product area of each employee (store clerk) so that the function F1 is small. In this system, the function F1 provides a basic numerical value in creating a shift table and a business instruction as an improvement measure.

  Next, the “time allocation” function F2 is a function for calculating the comparison of the work time allocation of the employee with another store using the second data in FIG. 3A as input, and is expressed by the following equation (2). .

式（２）中のＡは店舗Ａ、Ｂは優良他店舗Ｂのデータであることを示す。
時間配分といっても色々な定義があると思われるが、本実施例では、図３Ｂの円グラフに示すように、ある従業員がある時間帯ｔ（time zone）において、どの作業Ｔ（Task）に従事したかパーセンテージ値（%）で表した値を時間配分と読んでいる。

A in the formula (2) indicates that the data is from the store A and B is the data from the other excellent store B.
In the present embodiment, as shown in the pie chart of FIG. 3B, in the present embodiment, as shown in the pie chart of FIG. 3B, which work T (Task) in a certain time zone t (time zone). ) Or a percentage value (%) is read as time allocation.

The function F2 assumes an average value of a plurality of employees. A time distribution (%) of the work T in the time zone t at the store A is denoted as A _Tt . Similarly, store B is denoted as B _Tt . | A _Tt −B _Tt | is the absolute value of the difference between the two. F2 is a value obtained by integrating | A _Tt −B _Tt | in all work and all time zones. As the time distribution function F2 is larger, the ratio of how much time is spent in which work for each time zone is larger than the ratio of excellent stores. If it is assumed that the time distribution of work in an excellent other store with a large amount, profit rate, etc. is excellent, it indicates that there is much room for improvement in the time distribution of the store clerk at the own store. On the other hand, the smaller F2 is, the lower the time distribution of the store clerk compared with other excellent stores is.

For example, according to the pie chart of FIG. 3B, the ratio of the time zone A _1t in the store A to the entire time zone is larger than the ratio of the time zone B _1t in the store B to the entire time zone. It can be seen that there is room for improvement in the time distribution of this time zone.

  Therefore, the store manager may determine the work time distribution for each time zone of the employee (store clerk) so that the function F2 becomes small. In this system, the function F2 provides a basic numerical value in creating a shift table and a business instruction as an improvement measure.

  Subsequently, the function F3 of “motivation / energy” is a function for calculating the comparison of the employee's energy / energy with other stores by using the third data in FIG. 3A and is expressed by the following equation (3). It is.

式（３）中のＡは店舗Ａ、Ｂは優良他店舗Ｂのデータであることを示す。やる気・活力といっても色々な定義や測定方法（例えば、アンケートやヒヤリングといった従来からある方法、或いはセンサを用いた測定方法など）があると思われるが、本実施例では、センサとその関連ＩＴシステムを用いて測定する例を示す。本実施例では、以後、特に断りのないかぎり、センサとは、当該センサとその関連ＩＴシステムを示すものとする。センサからは、インタラクションデータ、人物間の関連性、行動指標又は積極アクティブ度と集中継続時間などの分析指標を得ることができ、本願明細書における「やる気・活力」の値として、これらの分析指標を多面的に用いることができるものとする。関数Ｆ３では、複数の従業員の平均値を想定している。店舗Ａでの時間帯ｔにおける従業員のやる気・活力（Activity Level）をＡ_Atと記す。店舗Ｂについても同様にＢ_Atと記す。div（Ａ_At, Ｂ_At）は、両者の割り算（division）、すなわち、Ａ_At／Ｂ_Atである。関数Ｆ３は、1/ div（Ａ_At, Ｂ_At）を全時間帯で積分した値である。このdiv（Ａ_At, Ｂ_At）が大きいほど、他店舗Ｂと比較して自店舗Ａの従業員（店員）のやる気・活力が高いことを示す。ただし、関数Ｆ１とＦ２が値が小さいほど店舗運営が良好であることを示す関数となっているので、本来は値が大きいほど良いdiv（Ａ_At, Ｂ_At）の逆数をとることで、関数Ｆ１とＦ２と同様に関数Ｆ３でも値が小さいほど店舗運営が良好である、店員のやる気・活力が高いことを示す関数となるように定義を揃えている。反対にＦ３の値が大きいほど店員のやる気・活力が低いことを示している
そこで、店長は、関数Ｆ３が小さくなるように、従業員（店員）のモチベーション向上を図る施策を実施すれば良い。本システムにおいて、改善策としてのシフト表や業務指示書の作成において、基本となる数値を提供するのが関数Ｆ３である。

In the formula (3), A indicates that the data is from the store A, and B is the data from the excellent other store B. There may be various definitions and measurement methods (for example, conventional methods such as questionnaires and hearings, measurement methods using sensors, etc.) even though they are motivated and energetic. An example of measurement using an IT system is shown. In this embodiment, hereinafter, unless otherwise specified, the sensor indicates the sensor and its related IT system. From the sensor, it is possible to obtain analysis indexes such as interaction data, relationships between persons, behavioral indicators, or active activity and concentration duration, and these analytical indicators are used as the value of “motivation / energy” in this specification. Can be used in many ways. In function F3, an average value of a plurality of employees is assumed. The employee's motivation / activity (activity level) in the time zone t at the store A is denoted as A _At . The store B is similarly written as B _At . div (A _At , B _At ) is a division between them, that is, A _At / B _At . The function F3 is a value obtained by integrating 1 / div (A _At , B _At ) in all time zones. The larger this div (A _At , B _At ) is, the higher the motivation and vitality of the employee (store clerk) of the own store A is compared to the other store B. However, since the functions F1 and F2 are functions indicating that the smaller the value, the better the store operation, the larger the value, the better the function div (A _At , B _At ) Similar to F1 and F2, the function F3 is also defined to be a function that indicates that the smaller the value, the better the store management, and the higher the motivation and vitality of the store clerk. Conversely, the greater the value of F3, the lower the motivation and vitality of the store clerk. Therefore, the store manager may implement measures to improve the motivation of the employee (the store clerk) so that the function F3 becomes smaller. In the present system, the function F3 provides a basic numerical value in creating a shift table and a business instruction as an improvement measure.

  Finally, the “efficiency function” F4 is a function for calculating the comparison between the sales amount and the employee work amount with the fourth data in FIG. 3A as input, and is expressed by the following equation (4).

式（４）中のＲは代表的なリターン（Return）である売上金額（Revenue）を、Ｔは店員作業量（Task）を示す。ある商品エリアa、時間帯tにおけるＲをＲ_at、ＴをＴ_atと記す。ここでは、Ｒ_atをＴ_atで割った値、すなわちＲ_at／Ｔ_atを、ＲＯＩ（リターンオンインベストメント：投資収益率）の類推から、ＲＯＷ_at （リターンオンワーク：作業収益率）と本願明細書では名付けている。関数Ｆ４は、1/ＲＯＷ_atを全時間帯、全エリアで積分した値である。このＲＯＷ_atが大きいほど、従業員（店員）の作業量に対し、売上額が大きいことを示す。ただし、関数Ｆ１とＦ２の値が小さいほど店舗運営が良好であることを示す関数となっているので、本来は値が大きいほど良いＲＯＷ_atの逆数をとることで、関数Ｆ１、Ｆ２、Ｆ３と同様に、関数Ｆ４でも値が小さいほど店舗運営が良好である、作業量に対し効率良く売上が上がっていることを示す関数になるように定義を揃えている。反対に関数Ｆ４の値が大きいほど効率が低いことを示している。

In the formula (4), R represents a sales amount (Revenue) which is a typical return, and T represents a salesclerk's work amount (Task). R in a product area a and time zone t is denoted as R _at , and T is denoted as T _at . Here, the value obtained by dividing R _at by T _at , that is, R _at / T _at is calculated from ROW _at (return on work: return on work) and the present specification from the analogy of ROI (return on investment: return on investment). Then I named it. The function F4 is a value obtained by integrating 1 / ROW _at in all time zones and all areas. The larger this ROW _at , the larger the sales amount relative to the work amount of the employee (clerk). However, since the functions F1 and F2 are smaller, the function indicates that the store operation is better. Therefore, the larger the value, the better the inverse of ROW _at , so that the functions F1, F2, F3 and Similarly, in the function F4, the smaller the value is, the better the store operation is, and the definition is aligned so that the function indicates that the sales are efficiently increased with respect to the work amount. On the contrary, the larger the value of the function F4, the lower the efficiency.

  Therefore, even if the value of the function F4 is large even if the values of the functions F1 to F3 are small, the store manager needs to consider measures for improving the business efficiency. In this system, the function F4 provides a basic numerical value in creating a shift table and a business instruction as an improvement measure.

  Note that thresholds Th1, Th2, Th3, and Th4 can be set for the functions F1, F2, F3, and F4 shown in the equations (1) to (4), respectively. As the threshold value, an average value or median value of a plurality of actually measured values from F1 to F4 can be used, and other values can be freely set. This threshold value is set in common for all stores based on, for example, “external factors” at the headquarters. If the value of F1 at a certain store is greater than or equal to Th1, it is “large”, and if it is less than Th1, it is “small”. Similarly, “large” and “small” can be obtained for F2 to F4. Assuming that each of the four functions F1 to F4 is “large” or “small”, there are 16 combinations (types) of 2 to the 4th power. In this embodiment, four functions from F1 to F4 are clearly shown. However, if only three of them are used, there are nine functions, and new functions such as F5 and F6 are added on the contrary. You can also The larger the function, the larger the number of combinations, and it is expected that detailed situation pattern classification will be possible.

  The situation pattern table 400 of FIG. 4 shows 16 situation patterns (types) composed of combinations of “large” and “small” compared with the threshold values Th1 to Th4 for each of the functions F1 to F4. The status of each of the 16 patterns (types) is shown in the “Employee status” column. For example, in the situation pattern 1, since all of F1 to F4 are “large”, “location placement” is bad, “time allocation” is bad, “motivation / energy” is low, and “business efficiency” Indicates a low situation. Therefore, it can be said that the situation is “motivated / low energy and significant improvement in store management”.

  Create work schedules (work shift tables) and work instructions designed to improve “location / location” and “time allocation” as well as “motivation / energy” and “work efficiency” according to this analysis I do. In order to create a shift table, a shift table creation macro is executed, and to create a business instruction, a business instruction macro is executed. Each macro incorporates creation logic (called a solution in this embodiment). Each type of situation pattern in FIG. 4 is associated with a solution that is a prescription corresponding to each situation pattern. Each solution is structured with logic to improve the situation based on the situation judgment indicated by each situation pattern.

  In situation pattern 2 and later in Fig. 4, as well as situation pattern 1, according to the current situation analysis shown in the "Employee situation" column, a recommendation plan for work schedule table (work shift table) and work instructions is created by the IT system. By doing so, the store manager can be urged to change store operations. By using the situation pattern table as shown in FIG. 4, the store manager determines which of the situation pattern types shown in FIG. It makes it easier to understand how to improve store operations. In addition to showing the situation pattern, the manager's office work can be reduced by creating recommendation plans for work schedules (work shift tables) and work instructions. Further, by creating a schedule table (shift table) and business instructions in combination with past examples and examples of other stores, it becomes easy to incorporate know-how of other stores into the own store. Also. Even if the store manager is replaced by personnel rotation, the know-how of the past store manager can be taken over.

  Here, a shift table creation macro for creating a schedule table (shift table) suitable for each situation pattern in FIG. 4 will be described. The shift table creation macro includes two types of rule sets (hereinafter, rule set A and rule set B) and employee entry information. The rule set A is a rule set common to retail stores, and the rule set B is an individual rule set for store conditions. Employee entry information includes the types of employees in each store, such as regular employees, contract employees, and temporary employees (see Fig. 15), specialized sales floor types and skills, and information on the date and time of work. It is a list that contains information about employees that can be used to create a shift table. The latter employee entry information stores information on available resources, whereas the company-wide rule set stores binding conditions.

  The rule set A is, for example, a rule regarding the number of working hours, such as a regular employee who works either on weekends or holidays, or has a maximum of three consecutive working days for a long period of work. On the other hand, general-purpose rules such as rules regarding fairness of work such as making the number of early and late numbers fair are stored. When creating the shift table, the employee entry information is first referred to, and personnel who can attend work are picked up as candidates for each opening day. Subsequently, this rule set A is applied to narrow down the number of employees who can work from the viewpoint of the number of working hours of the candidates and the fairness of work.

  The rule set B is an individual rule set for making necessary improvements such as reviewing the arrangement for each employee when the current operation status is bad according to the store status pattern.

  Next, a business instruction macro for creating a business instruction that conforms to each situation pattern in FIG. 4 will be described. The business instruction macro is determined that (1) “place / arrangement”, (2) “time allocation”, (3) “motivation / energy”, and (4) “work efficiency” are bad (low), respectively. In this case, it has a function to select the contents of business instructions related to each of these (1) to (4) and display them on the screen or to print them as distribution materials to employees. (See FIG. 16).

  FIG. 5 shows an example in which measurement at an actual store is performed using a sensor. FIG. 5A shows a sketch 501 of a certain store. There are two entrances on the left, followed by a cash register corner (shown as a square). Since various products are sold at the store, product shelves 502 are arranged at various locations in the store. As shown in FIG. 5B, each product shelf 502 is provided with a product 504 and a beacon 503 that communicates infrared rays 506 to the aisle at a certain time interval (for example, every 10 seconds). Yes. Each beacon is assigned an identification number, and is associated with an in-store product area, a product shelf, a product group, and the identification number. For this reason, the customer or employee wears the sensor and passes or stops near each beacon 503, whereby the in-store flow line information of the customer or employee can be stored in the IT system.

  As shown in FIG. 5C, infrared rays 506 are emitted from each beacon 503 and are detected by an infrared sensor built in the wearable sensor 505 worn by the customer. The wearable sensor 505 is a name tag type, but has high directivity, and can accurately measure which one the customer is facing. Therefore, the customer flow lines such as the flow line length and the stop-by rate in FIG. 2 can be measured with high accuracy. Subsequently, as shown in FIG. 5D, by providing the wearable sensor 505 with a function of communicating infrared rays 506, it is possible to detect whether the employee and the customer are facing each other. In addition, by incorporating an acceleration sensor (for example, a triaxial acceleration sensor) in the wearable sensor 505, the movement of the store clerk's body can be detected. As described above, by using the acceleration sensor and the infrared communication function, it is possible to visualize and quantify clerk duties such as customer service. Further, as shown in FIG. 5E, the store manager / manager wears a wearable sensor 505 having an acceleration sensor and an infrared communication function, thereby quantifying the communication amount between the store manager and the employee (store clerk). It is also possible.

  Among these, a working employee can be obtained by attaching a sensor to each employee and assigning a different management number (ID) to each sensor. The work content can be acquired by classifying the operation pattern by attaching a triaxial acceleration sensor to the employee sensor, for example. Alternatively, it can be obtained by image recognition using a camera. Next, the location arrangement can be obtained by, for example, attaching an infrared sensor to the employee sensor and arranging an infrared beacon at various locations in the store, and where the infrared sensor and the infrared beacon reacted. Subsequently, time distribution can be acquired by incorporating a clock function in the employee sensor and simultaneously recording the time at which the above-described three-axis acceleration sensor is acquired. Finally, data relating to human activities such as motivation and vitality can be acquired by the voice technique or the technique using an acceleration sensor described in the background art.

  FIG. 6 is a diagram illustrating a specific system configuration example of the store information system according to the first embodiment. The upper left part of FIG. 6 is a sensor group, and the store sensor related equipment group in the upper right part shows peripheral devices of the sensor group. The lower right part of FIG. 6 is a hardware configuration that processes the sensor signals acquired from the sensor group and executes the processes necessary for the present invention.

  The sensor group 600 in the upper left part of FIG. 6 includes, for example, a name tag type sensor 601, an RFID 602, an infrared sensor 603, an acceleration sensor 604, and the like. A name tag type sensor is, for example, a sensor having a compact shape of length (several centimeters) x width (several centimeters) x thickness (several millimeters to centimeters) of a name tag attached to each person's chest, It is a sensor 605 equipped with an acceleration, temperature, and infrared sensor. It has a memory function and can store information in the MEM section of the sensor 605. Data can be transmitted from these sensors to the wireless transmission / reception unit 606 via wireless and transferred to the communication device 615 via the Internet network 607.

  6 includes a system terminal 608, an application server 609, a cradle (for example, having a charging function and a data transfer function) 610, and a beacon 503 in FIG. The sensor 605 in FIG. 6 and the wearable sensor 505 in FIG. 5 show the same sensor in this embodiment. The data once stored in the sensor 605 of FIG. 6 is transferred to the communication device 615 via the Internet network 607, for example, by using the cradle transfer function by setting the sensor 605 in the cradle 610, for example.

  The hardware (server 100 and the like) constituting the main part of the store management support information generation system of this embodiment includes a display device 613 such as a display, an input device 614 such as a keyboard and a mouse, a communication device 615, a CPU 616, a memory 617, For example, it is composed of data and calculation program group 630 developed on the hard disk 618. Note that data once transferred from the sensor group or store sensor-related device group to the server 100 and the like, processed data thereof, and data such as intermediate files in the middle of calculation are stored by the data management server 612 and the data backup 611. The reason for this is to reduce the amount of communication through the Internet network 607 as much as possible and to store (back up) data twice.

  A headquarter server 640 is also connected to the Internet network 607.

  Data groups developed on the hard disk 618 constituting the main algorithm of the present invention are “face-to-face data” 619, “POS data” 620, “beacon / map data” 621, “acceleration data” 622, “situation pattern / good / bad”. Correlation determination pattern / countermeasure definition data ”623,“ store support function ”624,“ other store information / comfort factor etc. ”625,“ time series data / good / bad correlation information ”626 and“ template for improvement / shift table ” 627 and the like. The calculation program group 630 deployed on the hard disk 618 includes, in the order of execution, “read sensor data” 631, “time series conversion” 632, “first data to fourth data creation” 633, “F1 to F1”. F4 calculation ”634,“ situation pattern definition / classification / good / bad correlation determination ”635, and“ improvement proposal / shift table output ”636. “First data to fourth data creation” 633 are (1) first data related to “location / arrangement” in FIG. 3A, (2) second data related to “time allocation”, ( 3) Processing for creating third data related to “motivation / energy” and (4) fourth data related to “work efficiency”. Further, “F1 to F4 calculation” 634 is processing for calculating store support functions from F1 to F4. The “situation pattern definition / classification” 635 is a process of deriving “employee situation” and “measure” by comparing the calculation result of the store support function with the defined situation pattern table of FIG. That is.

The details of the relationship between each data group and the calculation program are shown in FIG. 7A.
FIG. 7A is a diagram in which a data group and a calculation program portion are extracted from FIG. A data group is shown on the left side of FIG. 7A, and a calculation program is shown on the right side. FIG. 7A shows an example in which data read from a storage medium such as a hard disk is expanded in the memory to increase the speed. First, “read sensor data” 631 reads data from sensors attached to employees, such as face-to-face data and acceleration data. In the next “time series conversion” 632, the data (face-to-face data and acceleration data) from the sensor and the POS data are merged with their time and time intervals aligned to create a data table. Then, the data table is divided every [opening time to closing time × N days].

  In “first data to fourth data creation” 633, for the first data creation, 1) face-to-face data and beacon / map data are collated, and 2) the number of staying customers and the number of staying clerks are integrated for each area. Process. Regarding the next second data creation, 1) the face-to-face data, the beacon / map data, and the same data of other stores are collated, and 2) it is determined whether the job is in charge / non-charge for each time zone. Continuing on with the third data creation, 1) Calculate the motivation and vitality data from the acceleration data, 2) Check the motivation and vitality data of the store and other stores, and 3) The store and other stores for each time zone Accumulate motivation and vitality data. Regarding the final fourth data creation, 1) the above first data creation 2), second data creation 2), third data creation 4) and POS data are collated, and 2) every product every time zone The sales amount and the amount of work are matched.

  In “Calculate F1 to F4” 634 in FIG. 7A, the data of the number of customers and the number of employees are calculated by substituting data into Formula (1) for F1. For F2, the data is substituted into Equation (2), and the comparison of the work time distribution of the employee with other stores is calculated. About F3, data are substituted into Formula (3), and the amount of activity of an employee is compared with other stores. For F4, data is substituted into equation (4) to calculate the comparison between sales and employee work.

  In the “situation pattern classification” 635 of FIG. 7A, each threshold value is used for F1 to F4 and divided into “large” and “small”, and the result is one of the types of the situation pattern data shown in FIG. It is determined whether the shift table creation macro and the business instruction macro should be executed as countermeasures as to the situation of the employee, the situation of the employee, and the countermeasure.

  In “extract situation pattern pass / fail correlation for each employee” 636, a time-series correlation between the situation pattern pass / fail and the employee shift is obtained for each employee, and a highly correlated one is extracted.

  In the “generation / output of shift table” 637, in order to improve the situation pattern or maintain a good state by using highly correlated information, template information related to the shift table or past information is used. Read and actually execute the shift table creation macro and business instruction macro. As a result, a business instruction is generated for the shift table.

  FIG. 7B is a diagram illustrating an example of the definition of the pass / fail correlation determination pattern 6230 of the time-series change of the situation pattern. Here, the first pattern to the eighth pattern are defined. For example, the first pattern is a state in which the time pattern change of the situation pattern determined by the combination of the functions F1 to F4 maintains the “good situation pattern”. It is. The second pattern is a continuous improvement of the situation pattern, the third pattern is a continuous decline of the situation pattern, the fourth pattern is a bad situation pattern, the fifth pattern is a temporary improvement of the situation pattern, The sixth putter is a temporary decline in the situation pattern.

  FIG. 7C is a diagram showing an example of an improvement / decrease factor pattern 6231 in the first to sixth patterns of FIG. 7B. (A) shows a single factor (improvement), (B) shows a pattern of “single factor (decrease), and (C) shows a“ causal relationship of multiple factors ”. For example, regarding the fifth pattern in FIG. 7B in which “temporary situation pattern improvement” is observed, any one of the functions F1 to F4 is used for the temporary improvement as shown in FIG. The factors are extracted as to whether they contribute solely or whether multiple functions are involved in combination as shown in FIG. 7C.

  Next, the location arrangement optimization process in the store information system of the present embodiment will be described with reference to the flowchart of FIG. 800 is a flowchart for creating a schedule table (shift table) and the like based on the calculation program 630 of FIG. First, customer data (data relating to flow line length, stop-off rate, purchase rate, etc.) for each in-store area is collected from the customer sensor or POS system (S801). Subsequently, employee data (data relating to work personnel, work content, location arrangement, time distribution, motivation / energy, etc.) for each store area is collected from the employee sensor (S802). Further, information such as situation pattern definition / threshold Th / store information / rule set / good / bad correlation determination pattern is also acquired (S803).

  Next, the customer data and employee data at regular time intervals are aggregated (S804). By performing this aggregation process, data related to (1) “location / placement”, (2) data related to “time allocation”, and (3) data related to “motivation / energy” shown in FIG. 3A. And (4) data related to “work efficiency” can be created.

  Thereafter, the functions F1 to F4 shown in the equations (1) to (4) are calculated (S805), and the situation of the current store is compared with the situation pattern data shown in FIG. 4 by the combination of the magnitudes of F1 to F4. The pattern is determined (S806).

  FIG. 9 shows an example of a display screen of situation pattern information of a certain store. This display is displayed, for example, on the screen of an administrator's terminal such as a store. The upper part of the screen shows the transition of the pattern number (FIG. 4) during a certain month. In the lower part of the screen, the transition of each value of the functions F1 to F4, which is the basis for calculating the pattern number, is plotted. In the plot of F1 to F4, if it is plotted upward from the threshold line (location arrangement, time distribution) is good (motivation / energy, efficiency is high), it is plotted downward from the threshold line. (Location, time allocation) is poor or (motivation, vitality, efficiency) is low. For example, X month 1 (Monday) is classified into the situation pattern 10 according to FIG. 4 because the place arrangement: good, time distribution: bad, motivation / energy: high, efficiency: low. The situation pattern is calculated in the same manner after the second day. By using the screen as shown in FIG. 9, the store manager / manager can change the situation pattern in his / her store, the location of employees, time allocation, motivation / energy, and the quality of work efficiency (or high / low). It is possible to grasp in series.

  Typical patterns corresponding to a plurality of pass / fail correlation determination patterns are extracted from time-series data of situation patterns from the past to the present as shown in FIG. 9 (FIG. 8, S807). That is, a typical pattern corresponding to the “good / bad correlation determination pattern” 6230 of the time-series change of the situation pattern shown in FIG. 7B is extracted. If typical patterns corresponding to the first to sixth patterns in FIG. 7B are extracted, it is analyzed which of the “improvement / decrease factor patterns” 6231 shown in FIG. 7C corresponds to.

  Next, the presence / absence of time series correlation for each employee with respect to the extracted typical pattern / factor is determined, and pass / fail correlation information for each employee is generated (S808). That is, from the “improvement / decrease factor pattern” 6231 and the employee shift pattern, the presence / absence of a time-series correlation with the improvement / decrease factor pattern for each employee is determined. Generate correlation information with employee shifts. For example, if “motivational energy” contributes to “temporary situation pattern improvement” and the shift situation of a specific employee corresponds to this, these are recorded as highly correlated.

  Next, for example, in accordance with the “employee priority order” set in advance by the store manager, an improvement shift pattern reflecting the good / bad correlation information of each employee is generated (S809). By setting “employee priority”, it is possible to generate an improvement plan of a reasonable situation pattern in accordance with the priority order from a plurality of possible shift patterns.

  Further, a schedule table (shift table) is created based on the rule set and the improvement shift pattern of all employees (S810). The shift table is created in units of weeks, months, and days. In this embodiment, the shift tables can be called a next week shift table, a next month shift table, and a next day shift table, respectively.

  The store manager / manager can view these shift tables on the user interface displayed on the display device. These shift tables are displayed as recommendations on the user interface in the form of recommendations (S811). When the store manager / manager performs an approval process for this recommendation (S812), the shift table is finally fixed, and the fixed shift table is printed out by, for example, a printer. If the manager / manager does not approve the recommendation, the process returns to the shift table creation process again, and the modified shift table is recommended.

  FIG. 10 shows an example of a screen displaying time series data of situation patterns. As shown in FIG. 4, there are zero judgments that the situation pattern 1 is good or high, one judgment that the situation patterns 2 to 5 are good or high, and two judgments that the situation patterns 6 to 11 are good or high. In the situation patterns 12 to 15, there are three judgments that are good or high, and in the situation pattern 16, there are four judgments that are good or high. Therefore, from the top to the bottom of the screen of FIG. 10, the situation pattern 16 is the top layer, the patterns 12 to 15 are the second layer, the patterns 6 to 11 are the third layer, the patterns 2 to 5 are the fourth layer, and the pattern 1 is Located in the bottom layer. The numbers in each black circle represent the date, and the pattern number for each date is the same as the content shown on the pattern information display screen of FIG. When the screen transitions from the lower layer to the upper layer, it is considered that the store management situation has improved, so it is displayed with a thick solid line. On the other hand, when a transition from the upper layer to the lower layer is made, it is considered that the store management situation has not improved, and therefore, it is indicated by a dotted line. Moreover, since it is thought that the store management situation is maintained when it changes between the same layers, it is displayed with the thin continuous line.

  In the example of FIG. 10, for example, from 8th → 9th → 10th → 11th, the screen continuously changes from the lower layer to the upper layer, and the store management situation is continuously improved. Can be seen. By viewing the shift table (plan) and shift table (actual results) from the 8th to the 11th, it is possible to look back at how employees worked and give managers and managers awareness. It can be an opportunity.

  In FIG. 11, the example of the screen of the comparison display with the other store regarding store management is shown. The contents of the left half of FIG. 11 are the same as the display contents of FIG. The content on the right half of FIG. 11 is a diagram relating to store C of another store that has increased sales and profit margins. In store C, plots are gathered in the upper layer of the figure, and in store C, it can be seen that the location of employees, time allocation, motivation / energy, and efficiency are maintained at a high level. Conventionally, efforts such as analyzing good stores, formalizing them as best practices, and creating guidelines for other stores have been made, but according to the present invention, it can be compared with multi-faceted other stores. , Making it easier to share best-practice initiatives across stores.

  FIG. 12 shows an example of typical pattern extraction in the period from October 1, 2012 to October 31, 2012. For example, a good pavement management state is maintained from the 15th to the 20th, which corresponds to the “first pattern”. Further, the state of continuously transitioning from the lower layer on the screen to the upper layer for 8 days → 9 days → 10 days → 11 days corresponds to the “second pattern”. Similarly, “third pattern”, “fifth pattern”, “sixth pattern”, and “seventh pattern” are also extracted.

  FIG. 13 is a diagram illustrating an example of a flowchart 1300 for obtaining extracted typical patterns and employee time-series correlation information. First, a search period, an extracted typical pattern, an employee name, and the like are input as search conditions (S1301). Next, it is checked whether there is a match with the “extraction typical pattern” in the shift information one week ago (S1302), and if it matches, the related data at that time is accumulated (S1303). Similarly, it is checked whether there is a match in the shift information two weeks ago (S1304), and whether there is a match in the shift information before two weeks (S1306). Data is accumulated (S1305, S1307). To do. Finally, these are put together, employee time-series correlation information for the extracted typical pattern / factor is generated (S1308), information necessary for the store manager or the like is registered, and the process ends.

  Next, referring to FIG. 14, a process of determining an extracted typical pattern / factor and generating an improved shift pattern based on the result will be described. First, as a determination condition, a period and an employee name are input (S1401), and it is determined whether there is a time-series correlation of employees having correlation with the pass / fail correlation determination pattern of FIG. 7B (S1402). If there is, the factor to be improved regarding the employee is analyzed based on the improvement / decrease factor pattern of FIG. 7C (S1403). Further, it is also possible to grasp each employee's assigned / non-assigned time zone (S1404).

  According to the rule set B, for example, in the case of the eighth pattern in FIG. 7B, (1) “location / arrangement”, (2) “time allocation”, (3) “motivation / energy”, and (4) Since all of “work efficiency” are poor or low, it is necessary to create a shift table and a work instruction by raising the priority order of any of these (1) to (4). On the other hand, for example, in the case of the situation pattern 12 of FIG. 4, only (1) “place location” is bad, and the other (2) to (4) are in good condition. Therefore, (1) “location arrangement” is shifted in accordance with the store visitor for each customer time zone. In this case, it is necessary to shift the “placement arrangement” for employees who are highly correlated with the situation pattern 12. Further, for example, in the case of the situation pattern 13, (2) only “time allocation” is bad, and the other (1), (3), and (4) are currently in good condition. If the employee who has a high correlation with this pattern has a lot of support to the non-responsible department, increase the allocation of the department where the support is gathered (S1405) and reduce the support time to the non-responsible department as much as possible. Processing is executed (S1406). Further, for example, in the case of the seventh pattern in FIG. 7B, since all of (1) to (4) are good or high, it is sufficient to try to maintain the current situation including the arrangement of employees. In this way, the next week / next month / next day shift table is created (S1407), and is displayed on the store manager's terminal as a recommendation along with comments regarding improvement (S1408). In this way, the process is repeated until the store manager's approval is obtained (S1409), and the improvements and changes are recorded as the rule set B, and the approved shift table is put into execution.

  FIG. 15 is an example of a shift table displayed on the user interface according to the flowchart of FIG. FIG. 15 shows an example of a next day shift table that is a shift table created on a daily basis. Information representing the creation range of the shift table such as date, store name, product name, and sales floor name is displayed at the top of the shift table. The column of the shift table in the center of the screen is displayed in the order of name, type, shift, start time, time zone display, and remarks column from the left. In addition, auxiliary total information such as the number of people and the attendance rate (%) for each time zone is displayed at the bottom of the screen. There is a check box column at the left end of the shift table (next to the name column), which can be used to approve shift table information of some employees. In the name column, information such as the employee's name or staff or part-time job is always displayed. The type column is recommended (next day), actual result (previous day), and plan (previous day) from the top. The recommendation (next day) is a draft of the shift table for the next day created by the system, meaning that the actual result (previous day) is the actual value of the previous day, and the plan (previous day) is the planned value approved on the previous day. The shift column displays information such as whether the employee's work shift is the first shift (S1) or the second shift (S2). In the start time column, the start time corresponding to the normal shift is displayed. In the time zone display, any one of front (in charge), front (not in charge), backyard, office work, break, etc. as shown in the legend is displayed in different colors. The store manager / manager determines whether to approve the recommendation for the next day with reference to the plan and results of the previous day, and presses an “approval” button to approve the recommendation. If it cannot be approved, press the “Reorganize” button and create a new shift table plan according to the screen. Alternatively, when the next day shift table creation work itself is interrupted, it is possible to move to another screen by pressing a “return” button.

  As described above, by applying the rule set B after the application of the rule set A, a new shift table can be created under more detailed conditions added to the rule set A. As described above, a new shift table corresponding to the situation pattern of FIG. 4 is automatically created, thereby reducing the office work burden of the store manager / manager.

  FIG. 16 shows a system of business instructions in the store information system of the present invention. Based on this system of business instructions, a business instruction is created by a business instruction macro suitable for each situation pattern in FIG. For example, when it is determined that (1) “place / arrangement”, (2) “time allocation”, (3) “motivation / energy”, and (4) “work efficiency” are bad (low), respectively, As shown in FIG. 16, the contents of business instructions related to each of these (1) to (4) are selected and displayed on the screen, or printed as distribution materials to employees. By linking business instructions with POS data, it is also possible to display information such as sales trends by product. The example of FIG. 16 divides the contents of business instructions to employees into space management (in-store space management) and in-store promotion (in-store sales promotion activities) from the viewpoint of store operation, and the former is further divided into floors (floors). It is divided into management and shelf management. Among these, when (1) “location / arrangement” is bad, a business instruction regarding improvement of space management considered to be mainly related is given. Next, when (2) “time allocation” is poor, a business instruction regarding improvement of in-store promotion, which is considered to be mainly related, is given. Next, (3) “Motivation / Vitality” is designed to give business instructions for both space management and in-store promotion. Finally, (4) “Working efficiency” is also instructed for both space management and in-store promotion.

  About the contents of the business instructions in FIG. 16, more specifically, for floor management, for example, shelves of items that are highly likely to be purchased together (such as tea and lunch boxes) are placed close to each other. Content such as zoning based on sales trends and considering the installation of collection points may be displayed or printed out alone or in combination. In addition, regarding shelf management, contents such as placing focused products near each other on the shelf, paying attention to missing items, and performing early ordering processing are displayed alone or in combination or printed out. Then, as contents common to floor management and shelf management, for example, 5S activities such as organizing and organizing, inquiries to the headquarters, etc., the contents of business instructions are displayed alone or in combination or printed. Finally, for in-store promotions, for example, the use of POP, conscious customer service, implementation with salesperson valve, etc. are displayed or printed out alone or in combination.

  As described above, the business instructions according to the situation pattern of FIG. 4 are automatically created, so that the office work burden of the store manager / manager can be reduced.

  The effects of this embodiment can be summarized in the following four points. (1) The store manager can comprehensively evaluate the current state of the store he / she is responsible for based on patterns. (2) Since it can be compared with other excellent stores etc. by pattern, the direction of change can be shown for improving the own store, and a concrete change can be promoted. (3) It can lead to the creation of a prescription that is flexible and fits the store manager's on-site sensation. (4) It is possible to support the creation of a shift table and business instructions that are easy for the store manager to operate, and to secure time for the store manager to stay at the site.

  In the present invention, data and numerical processing functions necessary for management of store employees are embodied and incorporated in the IT system. By using the present invention, it is possible to provide a flexible and easy-to-handle system that matches the store manager's on-site sensation. The running cost is low, and it can be easily compared with other excellent stores etc. according to the situation pattern, and by showing specific measures, the store manager is shown a specific direction and encourages actions to improve the store. It is expected.

  INDUSTRIAL APPLICABILITY The present invention can be used to improve profits at actual stores and improve the operating efficiency of actual stores. However, in addition to sales of the products described in the first embodiment and stores such as restaurants, a plurality of companies that perform the same business under one company organization, such as the medical field of nursing care / hospital and the educational field such as a cram school, etc. Use the present invention in a field where there are affiliated stores and facilities (each store of the business system) and a plurality of employees share a series of related tasks (providing services) in each store. Is possible.

  In other words, by combining data necessary for managing employees of each affiliated store or facility (each store of the business system) in these fields and a numerical processing function, the state of the business system is the same as in the first embodiment. Is created in a multifaceted manner, and a table comprising the situation pattern information and countermeasures is constructed. By using the table, the IT system can automatically create a schedule (shift) table and a business instruction that appropriately correspond to the situation of each store, and can support the person in charge of operating the business system.

DESCRIPTION OF SYMBOLS 100 ... Server, 101 ... Input processing part, 102 ... Output processing part, 103 ... Store situation pattern / countermeasure definition part, 104 ... Store support function (F1-F4) calculation part, 105 ... Store using store support function Situation pattern determination unit, 106 ... productivity improvement information generation unit, 107 ... communication processing unit, 110 ... database holding information of "store support function", 111 ... "store situation pattern / good / bad correlation determination pattern / correspondence Database that holds information of “plan definition”, 112... Database that holds “other store information”, 113... Database that holds information of “external factor”, 114 ... information of “time series data / good / bad correlation” Database to hold, 115 ... Database to hold information of "shift table / business improvement", 120 ... Terminal for manager, 131 ... Customer sensor, 132 ... Employee group Sa, 133 ... POS system, 140 ... store support function F1~F4.

Claims (15)

Multiple types of store support functions, which are a plurality of indicators indicating the operation state of a store based on a function given in advance, with information indicating an employee's behavior changing with time in a predetermined real space inside the store A store support function calculation unit for calculating
A pattern determination unit for determining which of the types of the situation pattern the output of the store support function matches;
A situation pattern storage unit for recording the calculated output values of the plurality of types of store support functions and the situation pattern as time series data; and
A pass / fail correlation information storage unit that records a plurality of pass / fail correlation determination patterns set in advance;
A shift pattern storage unit for recording the shift pattern for each employee and the time series data of the work situation;
A productivity improvement information generator,
The plurality of types of store support functions are:
The work status of each employee, the work content of each worker, location arrangement, time allocation, composed of data representing motivation and vitality, and data representing the sales status of the store,
The situation pattern determination unit determines which one of the combination of the plurality of types of store support functions calculated in the store support function calculation unit matches the type of the situation pattern,
In the productivity improvement information generation unit,
A typical pattern corresponding to the pass / fail correlation determination pattern is extracted from the time series data of the situation pattern, the presence / absence of a time series correlation between the typical pattern and the work situation for each employee is determined, and the time series correlation is calculated. A store management information system, characterized in that the store management information system generates information related to the store management improvement. In claim 1,
Data representing the work status of the employee is acquired through the sensor held by the employee,
Data representing a sales situation of the store is acquired via the POS system and a sensor corresponding to the customer in the store. In claim 2,
In combination with the customer sensor or the data of the POS system, the sales of the store excluding fluctuations due to external factors,
In combination with the sensor data held by the employee and the POS system data, the sales of the store excluding fluctuations due to external factors are obtained,
A store management information system that analyzes the relationship between sales results and store operation status from the relationship between the sales amounts and generates a work schedule for employees of the store. In claim 2,
The store support function includes a place arrangement function F1, a time distribution function F2, a motivation / energy function F3, and an efficiency function F4. In claim 4,
The place arrangement function F1 is:
For each product, product group, product area in the store, the number of staying customers and the number of staying employees for each time period are input data, and the comparison between the number of customers and the number of employees is calculated.
The time distribution function F2 is:
For each employee, as the input data, the work type for each time zone,
Calculating a comparison with other stores of the work time distribution of the employee,
The motivation and vitality function F3 is
For each employee, the amount of activity for each time zone is used as input data.
Calculating the amount of activity of the employee with other stores;
The efficiency function F4 calculates the comparison between the sales amount and the employee work amount by using the sales amount and store work amount for each product, product group, and product area in the store as input data. A store management information system characterized by In claim 4,
The predetermined plurality of pass / fail correlation determination patterns include at least a first pattern in which a time-series change of a situation pattern determined by a combination of the functions F1 to F4 maintains a good situation pattern, A second pattern that is a continuous improvement state, a third pattern that is a continuous decline of the situation pattern, a fourth pattern that maintains a bad situation pattern, a fifth pattern that is an improvement of the temporary situation pattern, and a temporary The store management information system characterized by including the 6th pattern which is a fall of a serious situation pattern. In claim 4,
With a display,
The situation pattern data is classified into situation pattern types according to the magnitudes of the outputs of the store support functions F1, F2, F3, and F4, and stored in the situation pattern storage unit.
Employee situation determination for each situation pattern type, as a countermeasure against the situation, a macro for creating a schedule table for improving the situation, a function for displaying the schedule table on the display unit, and the display A store management information system comprising a macro for creating a business instruction in a department and a function for displaying the business instruction. In claim 4,
With a display,
The situation pattern is generated as time series data of the situation pattern data by a combination of magnitude relations with respect to threshold values of the respective functions set in advance for the values of the functions F1, F2, F3 and F4.
The threshold value of each function is changed according to an external factor,
The store operation information system characterized in that the display unit has a function of displaying pattern information that can be compared with a transition based on the pattern type number. A server in the store,
A manager terminal configured to communicate with the server;
The server is a function realized by executing a program,
A situation pattern / countermeasure definition unit that defines a type of a situation pattern that represents the operation status of the store by a combination of a plurality of types of store support functions, and a countermeasure for the type;
A store support function calculator for calculating the plurality of types of store support functions;
A store situation pattern determination unit that determines which of the situation patterns the operation status of the store corresponds to using the calculated store support function;
Based on the type of the situation pattern of the store and the defined countermeasure, a productivity improvement information generating unit that generates operation improvement information of the store is provided,
The multiple types of store support functions are the work status of the employees of the store, the work staff, the work content, the location arrangement, the time allocation, the motivation and vitality, and the sales status of the store linked to the work purpose of the employee Is configured with data representing
The store situation pattern determination unit determines whether the output of the store support function obtained by calculation matches the type of the situation pattern,
The productivity improvement information generation unit automatically creates improvement information on the operation of the store according to the type of the situation pattern. In claim 9,
Data necessary for calculating the store support function,
A wireless or infrared communication device that can be attached to the employee and transmits information for recognizing the position and operation of the employee as radio waves or infrared rays, and
A presence position recognition unit that receives the wireless or infrared rays transmitted from the wireless or infrared communication device and recognizes the presence position and operation of the employee;
A store management information system, wherein the location and operation of the employee are collected from a storage unit that records the employee in time series. In claim 9,
Data necessary for calculating the amount of activity for each employee and each time zone,
Store operation information that can be attached to the employee, can acquire acceleration information during the operation of the employee, and collects the acceleration information from an acceleration measuring device having a transfer function for transferring the device outside the device. system. An operation information system applied to each store of a business system having a plurality of affiliated stores,
A plurality of indicators indicating the operating state of the business system based on a function given in advance with information indicating the working status of the employee and the behavioral status of the customer changing with time in a predetermined real space inside the store A store support function calculation unit for calculating a plurality of types of store support functions,
A pattern determination unit for determining which of the types of the situation pattern the output of the store support function matches;
A situation pattern storage unit for recording the calculated output values of the plurality of types of store support functions and the situation pattern as time series data; and
A pass / fail correlation information storage unit that records a plurality of pass / fail correlation determination patterns set in advance;
A shift pattern storage unit for recording the shift pattern for each employee and the time series data of the work situation;
A productivity improvement information generator,
The plurality of types of store support functions are:
The work status of each employee, the work content of each worker, location arrangement, time allocation, composed of data representing motivation and vitality, and data representing the sales status of the business system,
The situation pattern determination unit determines which one of the combination of the plurality of types of store support functions calculated in the store support function calculation unit matches the type of the situation pattern,
In the productivity improvement information generation unit,
A typical pattern corresponding to the pass / fail correlation determination pattern is extracted from the time series data of the situation pattern, the presence / absence of a time series correlation between the typical pattern and the work situation for each employee is determined, and the time series correlation is calculated. An operation information system for a business system, characterized in that information on operation improvement of the business system is generated. In claim 12,
In the display screen group accessible by the store manager,
By plotting the values of the plurality of types of store support functions on the same screen as the transition by the pattern type number in a certain period, with reference to each preset threshold value, the number of the pattern type A business system operation information system characterized by having a pattern information display function that can be compared with the transitions of the system. In claim 13,
As a function to display improvement information of the business system using the pattern data,
In the screen group,
A business characterized by displaying a state in which the degree of goodness of the pattern type is high and a state in which the degree of goodness of the pattern type is low, aligned on the screen and visualizing a time series change in the degree of improvement of the pattern type System operation information system. In claim 13,
In the screen group,
An operational information system for a business system that visualizes a situation comparison with another business system by displaying the own business system and another business system in parallel on the same screen.

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