Plan analysis device, plan analysis method, and plan analysis program

Abstract

To extract, across multiple stores such as in the retail industry, comparison sites that are more likely to be a reference for a target site, and display work plans of the target site and the extracted comparison sites.SOLUTION: A plan analysis device comprises a memory that stores work plan information for each of multiple cluster classifications in multiple sites, and a processor, and is configured to: cluster the multiple sites for each of multiple pieces of cluster classification information; compute productivity information for the multiple sites for each of the multiple pieces of clustered cluster classification information; identify analysis-element information related to productivity information whose correlation with the productivity information is at or above a threshold for each of the multiple pieces of cluster classification information; identify a model site using the analysis-element information for each of the multiple pieces of cluster classification information; identify the model site in the same cluster classification information as a target site; and output information regarding work plan information of the target site and the model site.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a plan analysis device, a plan analysis method, and a plan analysis program. [Background technology]

[0002] Patent Document 1 describes a business management support system for the retail industry that extracts and aggregates useful information on the sales performance and cost management of each store in a timely manner, and efficiently analyzes that information to present useful information on the business status of each store to the user in real time and in a manner that allows comparison with other stores. In this case, when displaying the business management information, the business information of multiple stores within a surrounding area within a specified distance from the location of a specific store is displayed so that the stores can be compared. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-133277 Summary of the Invention [Problem to be solved by the invention]

[0004] However, simply comparing multiple stores within a certain distance of each other simply means that the stores happen to be located close to each other, and the comparison may not actually be meaningful. In other words, it is unclear whether the comparison will be useful for the target location.

[0005] The purpose of the present disclosure is to provide a technology that can extract comparison locations that are more likely to be useful as references for a target location, for example, among multiple stores in the retail industry, and display work plans for the target location and the extracted comparison locations. [Means for solving the problem]

[0006] A planning analysis device according to one aspect of the present disclosure includes a memory that stores work plan information for multiple cluster classifications at multiple locations, and a processor, and the processor works in cooperation with the memory to cluster the multiple locations according to the multiple cluster classification information, generate productivity information for the multiple locations for each of the cluster classification information, identify analysis element information related to productivity information that has a correlation with productivity equal to or greater than a threshold for each of the multiple cluster classification information, identify a model location using the analysis element information for each of the multiple cluster classifications, identify the model location in the same cluster classification information as a target location, and output information related to the work plans for the target location and the model location.

[0007] A plan analysis method according to one aspect of the present disclosure clusters the multiple bases according to the multiple cluster classification information, generates productivity information for the multiple bases for each of the clustered multiple cluster classification information, identifies analysis element information related to productivity information that has a correlation with productivity equal to or greater than a threshold for each of the multiple cluster classification information, identifies a model base using the analysis element information for each of the multiple cluster classifications, identifies the model base in the same cluster classification information as a target base, and outputs information regarding work plans for the target base and the model base.

[0008] A planning analysis program according to one embodiment of the present disclosure causes a computer to perform the following steps: cluster the multiple locations according to the multiple cluster classification information; generate productivity information for the multiple locations according to the clustered multiple cluster classification information; identify analysis element information relating to productivity information whose correlation with productivity is equal to or greater than a threshold value for each of the multiple cluster classification information; identify a model location using the analysis element information for each of the multiple cluster classifications; identify the model location in the same cluster classification information as a target location; and output information regarding work plans for the target location and the model location.

[0009] These comprehensive or specific aspects may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a recording medium, or may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium. [Effects of the Invention]

[0010] According to the present disclosure, for example, in a retail industry or other multiple stores, it is possible to extract comparison locations that are more likely to be useful as reference for a target location, and display work plans for the target location and the extracted comparison locations. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a plan analysis system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a diagram illustrating a task item according to the present embodiment. [Figure 3] FIG. 1 is a diagram illustrating cluster classification according to the present embodiment. [Figure 4] FIG. 1 is a diagram for explaining the identification of analytical elements related to productivity according to the present embodiment. [Figure 5] FIG. 1 is a diagram illustrating how analysis elements are identified and model locations are identified for each cluster classification according to the present embodiment. [Figure 6] FIG. 1 is a diagram illustrating the identification of model locations for each cluster classification according to the present embodiment. [Figure 7] FIG. 10 is a diagram illustrating a comparison of productivity between a recommended store and a store according to the present embodiment. [Figure 8] FIG. 10 is a diagram illustrating a comparison of the work plans of a recommended store and the store itself according to the present embodiment. [Figure 9] FIG. 10 is a diagram illustrating a comparison of the work plans of a recommended store and the store itself according to the present embodiment. [Figure 10] FIG. 1 is a diagram illustrating planning analysis in various industries according to the present embodiment. [Figure 11] FIG. 1 is a diagram showing an example of a flow of a plan analysis according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described in detail with appropriate reference to the drawings. However, more detailed description than necessary may be omitted. For example, detailed descriptions of well-known matters and redundant descriptions of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Note that the accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0013] (Embodiment 1) <About the Planning Analysis System> First, the plan analysis system 1 will be described. Fig. 1 is a diagram showing an example of the configuration of the plan analysis system according to this embodiment. Fig. 2 is a diagram explaining task items according to this embodiment. Fig. 3 is a diagram explaining cluster classification according to this embodiment.

[0014] The plan analysis system 1 includes a plan analysis device 100, a plan generation device 200, a prediction system 300, multiple worker terminals 400, and multiple store / headquarters terminals 500. The plan analysis device 100, the plan generation device 200, the prediction system 300, the worker terminals 400, and the store / headquarters terminals 500 can transmit and receive information and data to and from each other via wired cables or a communication network. Examples of wired cables include HDMI (registered trademark) cables and USB cables. Examples of communication networks include wired LAN, wireless LAN, LTE, 4G, 5G, the Internet, and Virtual Private Network (VPN).

[0015] The plan analysis system 1 is used in a retail company with multiple stores, such as a supermarket, to analyze and evaluate a previously created staffing plan by comparing it with a model store or model base. The assumed target retail company here has multiple stores and a headquarters that manages each store. In the plan analysis system 1, the plan analysis device 100 compares, analyzes, and evaluates the employee staffing plan and staffing plan pattern generated by the plan generation device 200 using information such as customer number forecast data, arrival and shipment plans, production plans, and work performance generated by the prediction system 300. The staffing plan and its analysis results can be viewed on the worker terminal 400 or the store / headquarters terminal 500. Based on the comparison and analysis results of the plan analysis device 100, the user can modify the staffing plan via the plan generation device 200. The plan analysis device 100, the plan generation device 200, the prediction system 300, the worker terminal 400, and the store / headquarters terminal 500 are electronic devices such as personal computers, servers, smartphones, and tablets. The plan analysis system 1 may be used to analyze and evaluate personnel plans in companies in the logistics industry. Employees include full-time employees, contract employees, part-time employees, and casual employees, regardless of the type of contract. In the case of the logistics industry, for example, a target company may have multiple transportation bases and a headquarters that manages each transportation base.

[0016] The plan analysis device 100 includes a processor 110, a memory 120, and a display unit 130. The processor 110 includes a clustering unit 111, a productivity calculation unit 112, an analysis element identification unit 113, a model base identification unit 114, and a comparison information output unit 115. The memory 120 stores work plan information 121, cluster classification information 122, productivity information 123, and analysis element information 124.

[0017] The processor 110 executes programs stored in the memory 120 to realize various functions. The processor 110 calculates each correlation value and outputs corresponding information, as will be described in detail later. Examples of the processor 110 include a central processing unit (CPU), a micro processing unit (MPU), a controller, a large scale integration (LSI), an application specific integrated circuit (ASIC), a programmable logic device (PLD), and a field-programmable gate array (FPGA). The plan generation device 200, the forecasting system 300, the worker terminal 400, and the store / headquarters terminal 500 may also be equipped with a similar processor.

[0018] The clustering unit 111 clusters multiple bases into multiple cluster classifications. A base is a unit such as a store, a department within a store, or a group. In other words, multiple bases may be a store or multiple departments within a store. There may be various groups such as a grocery group shown in FIG. 3, or other groups such as delicatessen and seafood. Cluster classification will be described in detail in the cluster classification information 122.

[0019] The productivity calculation unit 112 calculates the productivity of multiple locations for each of the clustered clusters. The unit for calculating productivity may be, for example, any of the units shown in FIG. 2. In this example, the tasks in one group, the food group, are broadly classified into stocking and ordering, and then sub-classified into room-temperature items, refrigerated items, frozen items, and so on, and each task is further divided into individual tasks. Productivity may be calculated by store, group, broad classification, sub-classification, task, or other unit. As shown in FIG. 3, productivity is calculated using, for example, the number of customers divided by man-hours, refrigerated load volume divided by man-hours, sales volume divided by man-hours, or maximum order quantity divided by man-hours. In other words, productivity may be calculated using any of the following: workload divided by man-hours, load volume divided by man-hours, number of customers divided by man-hours, or production volume divided by man-hours. A man-hour is one person and one hour. For example, if one person works for five hours, the man-hours are 5, and if three people work for two hours, the man-hours are 6.

[0020] The analysis element identification unit 113 identifies analysis elements whose correlation with the productivity calculated by the productivity calculation unit 112 is equal to or greater than a threshold for each cluster classification. Analysis elements are related to productivity. "Above the threshold" may be changed to "below the threshold" depending on the type of numerical value, and what is important is that the correlation with productivity is good. Details of analysis elements will be described later. The model base identification unit 114 identifies a model base using the analysis elements for each cluster classification. The comparison information output unit 115 identifies a model base in the same cluster classification as the target base, and outputs information regarding the work plans for the target base and the model base. The specific information to be output will be described later. It is preferable that information regarding the work plans for the target base and the model base be output in a manner that allows for comparison. In this way, by outputting information regarding the work plans for the target base and the model base, it is possible to analyze work plans that have already been generated by the target base.

[0021] The memory 120 stores computer programs and data used by the processor 110. The memory 120 may be configured with a volatile storage medium and / or a non-volatile storage medium, and may include a read-only memory (ROM) and a random access memory (RAM). The plan generation device 200, the forecasting system 300, the worker terminal 400, and the store / headquarters terminal 500 may also be provided with similar memories.

[0022] The work plan information 121 includes personnel plans and personnel plan patterns generated by the plan generation device 200. Personnel plan patterns may be generated on a daily or hourly basis, for example, by store, by team, by group, by major or medium classification, or by task (see FIG. 2). Personnel plans include monthly work plans, daily work plans, and work plans by staff member that are generated by allocating personnel (employees) based on personnel patterns.

[0023] The cluster classification information 122 includes at least one of day-of-week information 1221, size information 1222, format information 1223, and store characteristic information 1224. The day-of-week information 1221 is information that directly or indirectly indicates how the days of the week are clustered. For example, the day-of-week information 1221 may include at least one of weekday classifications based on different customer trends, such as weekdays, Saturdays, Sundays, and holidays, sale days (weekdays), sale days (Saturdays, Sundays, and holidays), and busy days. Saturdays, Sundays, and holidays may be divided into Saturdays, Sundays, and holidays. The size information 1222 is information that directly or indirectly indicates, for example, sales size, and in FIG. 3, sales are classified into six levels: very high, high, medium-high, medium-low, low, and very low. The number of classifications in the size information 1222 is not limited to six, and may be two or more levels. Furthermore, the scale information 1222 is information that directly or indirectly indicates aspects such as production scale and the size of the base area, and may include, for example, at least one of sales scale information, production scale information, and store or base area information. The format information 1223 is information that directly or indirectly classifies, for example, the format of a store, and here, stores are classified into supermarkets (street-front stores), discount stores, and malls (located within malls). The format information 1223 is not limited to store format, but may also be classified by industry, location, and the like. That is, the format information 1223 may include at least one of store format information, industry information, and location information. The store characteristic information 1224 is information that directly or indirectly classifies, for example, store characteristics, such as customer volume trends (customer characteristics) such as morning peak stores and afternoon peak stores, store characteristics such as stores with face-to-face sales and stores without face-to-face sales (in other words, store sales format), employee work characteristics depending on the store sales format, and employee work characteristics depending on the store sales strategy.

[0024] The productivity information 123 is information that directly or indirectly indicates the productivity for each cluster classification and each base generated by the productivity calculation unit 112. The analysis element information 124 includes at least one of man-hour ratio information 1241, employee skill information 1242, and work performance information 1243. The man-hour ratio information 1241 includes the man-hour input rate in any time period, such as early morning or night. The skill information 1242 includes information on the skills possessed by each employee. The work performance information 1243 is data on the actual work performance of employees.

[0025] The display unit 130 may include a liquid crystal display device, an organic EL device, or other display device. The display device 25 displays various data and information. The plan generation device 200, the forecasting system 300, the worker terminal 400, and the store / headquarters terminal 500 may also be equipped with similar display units.

[0026] In this way, the plan analysis device 100 comprises a memory 120 that stores work plan information 121 for each of multiple cluster classification information 122 at multiple locations, and a processor 110, and the processor 110 works in cooperation with the memory 120, whereby the clustering unit 111 clusters the multiple locations for each of the multiple cluster classification information 122, the productivity calculation unit 112 calculates the productivity of the multiple locations for each of the cluster classification information 122, the analysis element identification unit 113 identifies analysis element information 124 related to productivity whose correlation with productivity is greater than or equal to a threshold for each of the multiple cluster classification information 122, the model location identification unit 114 identifies a model location using the analysis element information 124 for each of the multiple cluster classification information 122, and the comparison information output unit 115 identifies a model location in the same cluster classification information 122 as the target location, and outputs information related to the work plan information 121 between the target location and the model location.

[0027] The plan generation device 200 uses information such as customer number forecast data, arrival and shipment plans, production plans, and work performance from the prediction system 300 to predict workload and man-hours, and generates at least one of a manpower plan and a manpower plan pattern. The manpower plan pattern indicates the minimum and maximum required man-hours for each day of the week information 1221. The manpower plan pattern may be generated for each store, team, or task on a daily or hourly basis. The manpower plan may be a monthly work plan, daily work plan, or work plan for each staff member, generated by allocating personnel (employees) based on the manpower pattern.

[0028] The forecasting system 300 generates information such as customer number forecast data, arrival and shipment plans, production plans, and work performance data.

[0029] The worker terminal 400 is a terminal used by each worker among the employees of each store. The store / headquarters terminal 500 is a terminal installed in each store and the headquarters. For example, if a company that operates a supermarket chain uses this plan analysis system 1, the plan analysis device 100, plan generation device 200, prediction system 300, and store / headquarters terminal 500 may be installed at the headquarters or some of the stores, and the worker terminal 400 is used by the target worker among the employees of each store.

[0030] <About cluster classification information> Next, cluster classification will be described. As shown in FIG. 3, the productivity calculation unit 112 calculates productivity by group and for each cluster classification information 122. As described above, the cluster classification information 122 includes day of the week information 1221, size information 1222, and form information 1223. In the example of FIG. 3, there are four classifications based on the day of the week information 1221, six classifications based on the form information 1223, and three classifications based on the form information 1223, resulting in 4 x 6 x 3 = 72 cluster classifications. That is, for example, the productivity of a supermarket store with "high" sales is calculated using four cluster classifications: weekdays, weekends, and holidays, sale days (weekdays), and sale days (weekends, and holidays). The productivity calculation unit 112 clusters multiple locations (stores) across the country using each cluster classification information 122 and calculates the productivity of each. Productivity is calculated, for example, using the index shown in FIG. 3.

[0031] <Identifying analytical element information> Next, the identification of the analysis element information 124 will be described. Fig. 4 is a diagram for explaining the identification of analysis elements related to productivity according to this embodiment. Fig. 5 is a diagram for explaining the identification of analysis elements and the identification of model bases for each cluster classification according to this embodiment. Fig. 6 is a diagram for explaining the identification of model bases for each cluster classification according to this embodiment.

[0032] The analysis element identification unit 113 prepares multiple analysis axes, such as "sales target achievement rate," "selling price change rate," and "waste rate," and calculates the correlation between each of the multiple analysis axes and productivity. A common method for calculating the correlation may be used. In FIG. 4, correlations were found among 10 analysis axes, such as "man-hour input ratio by time period" and "food_stocking setting ratio." The analysis element identification unit 113 then identifies the correlation axes that showed correlations as analysis element information 124. The analysis element identification unit 113 then prioritizes the correlation elements, as shown in FIG. 5. The model base identification unit 114 then extracts stores that exceed the thresholds of the analysis element information 124, starting with the highest priority, until it narrows down the selection to, for example, one store. The model base identification unit 114 then identifies the store with the highest correlation for each of the 72 cluster classifications. Here, each cluster classification is narrowed down to one store, but multiple stores may be narrowed down.

[0033] FIG. 6 shows a table of one store extracted from each cluster classification information 122. In FIG. 6, one store extracted from supermarket-type stores is shown for each sales scale (ultra-high, high, medium-high, medium-low, low, ultra-low) on weekdays, weekends, holidays, and sale days. Cluster classification information 122 for which no store could be extracted is left blank. The store shown in FIG. 6 becomes the model store (model base) for each cluster classification information 122.

[0034] <Comparison between target stores and model stores> Next, a comparative display between a target store and a model store will be described. FIG. 7 is a diagram illustrating a comparison of productivity between a recommended store and the store's own store according to this embodiment. FIG. 8 is a diagram illustrating a comparison of work plans between a recommended store and the store's own store according to this embodiment. FIG. 9 is a diagram illustrating a comparison of work plans between a recommended store and the store's own store according to this embodiment. FIGS. 7 to 9 are examples of information relating to work plans for a target base and a model base.

[0035] For example, suppose a staff member at Store A wishes to revise Store A's Saturday and Sunday work schedule to a better plan by comparing it with a model store. Store A has an extremely low sales volume and a store format of a supermarket, so Store B is extracted by the comparison information output unit 115 as a model store for that cluster classification (day of the week: Saturday and Sunday, sales volume: extremely low, store format: supermarket). Then, the comparison information output unit 115 outputs information such as those shown in FIGS. 7 to 9 and displays it on the display unit 130.

[0036] As shown in the display information of FIG. 7, the comparison information output unit 115 displays a scatter diagram showing the productivity and average daily sales of multiple stores in the same cluster. This scatter diagram allows the user to obtain information comparing the productivity of the target store (store A) and the model store (store B). The scatter diagram also allows the user to confirm the position of the staff of store A relative to the target store (store A) and the model store (store B) among stores in the same cluster. The display information of FIG. 7 also includes a table showing the productivity, sales, man-hours invested, workload, workload composition, etc. of multiple stores in the same cluster. This allows the user to confirm, even using numerical data, the position of the staff of store A relative to the target store (store A) and the model store (store B) among stores in the same cluster.

[0037] As shown in the display information in FIG. 8, the comparison information output unit 115 may display information comparing the productivity of the target store (Store A) and the model store (Store B), particularly information related to the work plan, in a bar graph. FIG. 8 displays information related to the work plan, such as the required man-hours and the required man-hour ratio, for each major category in FIG. 2, compared between the target store (Store A) and the model store (Store B). The required man-hours refer to the man-hours required to perform a task (how many man-hours are desired for each task) and are calculated by dividing the predicted number of customers by the number of customers per man-hour. A man-hour refers to one person and a unit of one hour. The "number of customers per man-hour" refers to the number of customers that one employee can serve per hour. The predicted number of customers per man-hour is calculated for each business day characteristic by accumulating past data on the number of customers per man-hour and using machine learning, as shown in FIG. 3. Furthermore, the required man-hours are not limited to the number obtained by dividing the predicted number of customers by the number of customers per man-hour, but may be the required man-hours after correction using monthly working hours, etc., or the required man-hours by day, time period, or task calculated by comparing with the staffing plan pattern set by the store. Furthermore, the required man-hours by day, time period, or task may be values manually corrected by the supermarket store manager. This allows the user to know which parts of the work plan for the target store need to be corrected.

[0038] As shown in the display information in Fig. 9, the comparison information output unit 115 may, similarly to the above, display information comparing the productivity of the target store (store A) and the model store (store B), particularly information related to work plans, by time in a bar graph. Furthermore, the comparison information output unit 115 may output the difference in the results of comparing the productivity of the target store (store A) and the model store (store B) as a numerical value. Furthermore, the comparison information output unit 115 may output the difference in required man-hours not only for each major category in Fig. 2, but also for each intermediate category and task.

[0039] Furthermore, the comparison information output unit 115 may display "improvement proposal information for the target base" based on information about the work plans between the target base (Store A) and the model store (Store B), in addition to information about the work plans between the target base and the model base. This improvement proposal information for the target base may include information about at least one of training, reassignment, and changes in working conditions. Information about training may be, for example, information that suggests increasing the number of staff who have acquired specific skills. Information about reassignment may be, for example, information that encourages the reassignment of staff based on conditions such as staff skills and working hours. Information about changes in working conditions may be, for example, information that encourages changing part-time staff to contract employees or full-time employees.

[0040] <Use in various industries> Next, applications in various industries will be described. Figure 10 is a diagram for explaining plan analysis in various industries according to this embodiment.

[0041] The plan analysis device 100 of this embodiment can be used not only in the retail industry described above, but also in the logistics industry (warehousing), accommodation service industry, and food and beverage industry. In this case, the plan analysis device 100 may use productivity indicators, cluster classification information 122 (day of the week information 1221, size information 1222, and form information 1223), and information on classification elements extracted from the analysis axis, as shown in Fig. 10. Furthermore, the plan analysis device 100 may also be used in industries not listed in Fig. 10.

[0042] <About the planning analysis method and planning analysis program> Next, the flow of the plan analysis method will be described. Fig. 11 is a diagram showing an example of the flow of the plan analysis according to this embodiment. The order of the flow may be changed unless there are restrictions. The following flow may be executed by a computer that executes a program.

[0043] The clustering unit 111 clusters multiple stores and locations into multiple cluster classification information 122 as shown in FIG. 3 (ST1). The productivity calculation unit 112 then calculates productivity information 123 for the multiple locations for each of the cluster classification information 122 (ST2). As shown in FIG. 3, the productivity information 123 is calculated, for example, by the number of customers divided by man-hours, the amount of refrigerated cargo divided by man-hours, sales divided by man-hours, or the maximum number of orders divided by man-hours. The analysis element identification unit 113 then identifies analysis elements related to the productivity information 123 that have a correlation with productivity equal to or greater than a threshold value for each of the multiple cluster classifications (ST3). The model location identification unit 114 then identifies a model location using the analysis elements for each of the multiple cluster classification information 122, as shown in FIG. 6 (ST4). The model location identification unit 114 then identifies a model location in the same cluster classification information 122 as the target location (ST5). 7 to 9, the comparison information output unit 115 outputs information relating to the work plan information 121 between the target base and the model base (ST10). In this way, by outputting the work plan information 121 between the target base and the model base, the processor 110 of the plan analysis device 100 can analyze the work plan information 121 that has already been generated by the target base.

[0044] (Addendum) The above description of the embodiments discloses the following techniques.

[0045] <Technology 1> The plan analysis device 100 includes a memory 120 that stores work plan information 121 for each of a plurality of cluster classifications at a plurality of bases, and a processor 110. The processor 110 works in cooperation with the memory 120 to cluster the plurality of bases into each of a plurality of cluster classification information 122, calculate productivity information 123 for the plurality of bases for each of the cluster classification information 122, identify analysis element information 124 related to the productivity information 123 that has a correlation with the productivity information 123 equal to or greater than a threshold for each of the plurality of cluster classification information 122, identify a model base using the analysis element information 124 for each of the plurality of cluster classification information 122, identify the model base in the same cluster classification information 122 as the target base, and output information related to the work plan information 121 between the target base and the model base.

[0046] This makes it possible to extract comparison locations that are more likely to be useful as reference for the target location, for example, in the retail industry, among multiple stores, and display the work plans of the target location and the extracted comparison locations.

[0047] <Technology 2> In the plan analysis device 100 described in Technique 1, the plurality of cluster classification information may include at least one of day of the week information, size information, base configuration information, and store characteristic information. This makes it possible to effectively display the work plans of the target base and the extracted comparison bases.

[0048] <Technology 3> In the plan analysis device 100 described in Technique 1 or 2, it is preferable that information regarding the work plans of the target base and the model base be output in a manner that allows comparison. This makes it possible to display the work plans of the target base and the extracted comparison bases in an easy-to-understand and effective manner.

[0049] <Technology 4> In the plan analysis device 100 described in any one of Techniques 1 to 3, the multiple bases are units of multiple departments within a store. This makes it possible to display the work plans of the target base and the extracted comparison bases in detail and effectively.

[0050] <Technology 5> In the plan analysis device 100 described in any one of Techniques 1 to 4, the analysis element information includes at least one of man-hour ratio information, employee skill information, and work performance information. This makes it possible to accurately extract comparison locations that are more likely to be useful as reference for the target location, and to display work plans for the target location and the extracted comparison locations.

[0051] <Technology 6> In the plan analysis device 100 described in any one of Techniques 1 to 5, productivity information is generated using any one of workload / man-hour, load / man-hour, number of customers / man-hour, and production volume / man-hour. This makes it possible to accurately extract comparison bases that are more likely to be useful as reference for the target base, and to display work plans for the target base and the extracted comparison bases.

[0052] <Technology 7> In the plan analysis device 100 described in any one of Techniques 1 to 6, the day of the week information includes at least one of weekdays, Saturdays, Sundays, holidays, and busy days. This makes it possible to accurately extract comparison locations that are more likely to be useful as reference for the target location, and to display work plans for the target location and the extracted comparison locations.

[0053] <Technology 8> In the plan analysis device 100 described in any one of Techniques 1 to 7, the scale information includes at least one of sales scale information, production scale information, and store or base area information. This makes it possible to accurately extract comparison bases that are more likely to be useful as reference for the target base, and to display work plans for the target base and the extracted comparison bases.

[0054] <Technology 9> In the plan analysis device 100 described in any one of Techniques 1 to 8, the base configuration information includes at least one of store configuration information, industry information, and location information. This makes it possible to accurately extract comparison bases that are more likely to be useful as references for the target base, and to display work plans for the target base and the extracted comparison bases.

[0055] <Technology 10> In the plan analysis device 100 described in any one of Techniques 1 to 9, information about the work plans of the target base and the model base is output as a bar graph. This makes it possible to display the work plans of the target base and the extracted comparison bases in an easy-to-understand manner.

[0056] <Technology 11> In the plan analysis device 100 described in any one of Techniques 1 to 10, in addition to information relating to the work plans of the target base and the model base, a scatter diagram comparing at least the productivity information of the target base and the model base is displayed. This makes it possible to display the work plans of the target base and the extracted comparison bases in an easy-to-understand manner.

[0057] <Technology 12> In the plan analysis device 100 described in any one of Techniques 1 to 11, in addition to information on the work plans of the target base and the model base, improvement proposal information for the target base based on information on the work plans of the target base and the model base is displayed. This makes it possible to display not only the work plans of the target base and the extracted comparison base, but also the improvement proposal information.

[0058] <Technology 13> In the plan analysis device 100 described in Technology 12, the improvement proposal information for the target base includes information on at least one of training, reassignment, and changes in working conditions. This makes it possible to clearly display not only the work plans of the target base and the extracted comparison bases, but also the improvement proposal information.

[0059] <Technology 14> The plan analysis method clusters a plurality of bases into a plurality of cluster classification information 122, calculates productivity information 123 of the plurality of bases for each of the clustered plurality of cluster classification information 122, identifies analysis element information 124 related to the productivity information 123 whose correlation with the productivity information is equal to or greater than a threshold for each of the plurality of cluster classification information 122, identifies a model base using the analysis element information 124 for each of the plurality of cluster classification information 122, identifies the model base in the same cluster classification information 122 as the target base, and outputs information related to work plan information 121 between the target base and the model base.

[0060] This makes it possible to extract comparison locations that are more likely to be useful as reference for the target location, for example, in the retail industry, among multiple stores, and display the work plans of the target location and the extracted comparison locations.

[0061] <Technology 15> The planning analysis program clusters multiple locations into multiple cluster classification information 122, calculates productivity information 123 of the multiple locations for each of the clustered multiple cluster classification information 122, identifies analysis element information 124 related to the productivity information 123 that has a correlation with the productivity information equal to or greater than a threshold for each of the multiple cluster classification information 122, identifies a model location using the analysis element information 124 for each of the multiple cluster classification information 122, identifies the model location in the same cluster classification information 122 as the target location, and outputs information related to work plan information 121 between the target location and the model location.

[0062] This makes it possible to extract comparison locations that are more likely to be useful as reference for the target location, for example, in the retail industry, among multiple stores, and display the work plans of the target location and the extracted comparison locations.

[0063] Although the embodiments have been described above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art can conceive of various modifications, alterations, substitutions, additions, deletions, and equivalents within the scope of the claims, and it is understood that these also fall within the technical scope of the present disclosure. Furthermore, the components in the above-described embodiments may be combined in any manner without departing from the spirit of the invention. [Industrial Applicability]

[0064] The technology disclosed herein can extract comparison locations that are more likely to be useful as reference for a target location, for example, from multiple stores in the retail industry, and display work plans for the target location and the extracted comparison locations. [Explanation of symbols]

[0065] 1 Planning and Analysis System 100 Planning Analysis Device 110 processors 111 Clustering Department 112 Productivity Calculation Unit 113 Analysis element identification part 114 Model Base Identification Department 115 Comparison information output section 120 memory 121 Work Plan Information 122 Cluster Classification Information 1221 Day of the week information 1222 Scale information 1223 Morphological information 123 Productivity Information 124 Analysis element information 1241 Person-hour ratio information 1242 Skill Information 1243 Work performance information 130 Display section 200 Plan Generation Device 300 Prediction System 400 Worker terminal 500 store / headquarters terminals

Claims

1. A memory for storing work plan information for each of a plurality of cluster classification information at a plurality of bases, and a processor, The processor, in cooperation with the memory, clustering the plurality of bases for each of the plurality of cluster classification information; generating productivity information of the plurality of bases for each of the plurality of cluster classification information obtained by the clustering; identifying analysis element information relating to productivity information whose correlation with productivity is equal to or greater than a threshold for each of the plurality of cluster classification information; identifying a model base station for each of the plurality of cluster classifications using the analysis element information; Identifying the model base station in the same cluster classification information as the target base station; outputting information about the work plans of the target base and the model base; Planning analysis device.

2. The plurality of cluster classification information includes at least one of day of the week information, scale information, base configuration information, and store characteristic information. The plan analysis device according to claim 1 .

3. Information about the work plans of the target base and the model base is output in a comparable manner. The plan analysis device according to claim 1 or 2.

4. The plurality of bases are units of a plurality of departments within a store. The plan analysis device according to claim 1 or 2.

5. The analysis element information includes at least one of man-hour ratio information, employee skill information, and work performance information. The plan analysis device according to claim 1 or 2.

6. The productivity information is generated using any one of the following: workload / man-hour, cargo volume / man-hour, number of customers / man-hour, and production volume / man-hour. The plan analysis device according to claim 1 or 2.

7. The day of the week information includes at least one of weekdays, weekends, holidays, and busy days. The plan analysis device according to claim 2 .

8. The scale information includes at least one of sales scale information, production scale information, and store or base area information. The plan analysis device according to claim 2 .

9. The base configuration information includes at least one of store configuration information, business type information, and location information. The plan analysis device according to claim 2 .

10. Information about the work plans of the target base and the model base is output in the form of a bar graph. The plan analysis device according to claim 1 or 2.

11. In addition to information about the work plans of the target site and the model site, displaying a scatter diagram comparing productivity information of at least the target base and the model base; The plan analysis device according to claim 1 or 2.

12. In addition to information about the work plans of the target site and the model site, displaying improvement proposal information for the target base based on information about work plans for the target base and the model base; The plan analysis device according to claim 1 or 2.

13. The improvement proposal information for the target base includes information on at least one of education, transfer, and change of working conditions. The plan analysis apparatus according to claim 12.

14. Clustering multiple locations according to multiple cluster classification information, generating productivity information of the plurality of bases for each of the plurality of cluster classification information obtained by the clustering; identifying analysis element information relating to productivity information whose correlation with productivity is equal to or greater than a threshold for each of the plurality of cluster classification information; identifying a model base station for each of the plurality of cluster classifications using the analysis element information; Identifying the model base station in the same cluster classification information as the target base station; outputting information about the work plans of the target base and the model base; Planning analysis methods.

15. Clustering multiple locations according to multiple cluster classification information, generating productivity information of the plurality of bases for each of the plurality of cluster classification information obtained by the clustering; identifying analysis element information relating to productivity information whose correlation with productivity is equal to or greater than a threshold for each of the plurality of cluster classification information; identifying a model base station for each of the plurality of cluster classifications using the analysis element information; Identifying the model base station in the same cluster classification information as the target base station; outputting information about the work plans of the target base and the model base; A planning and analysis program that allows a computer to carry out the following:

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