Regional service design support system, regional service design support method, and regional service design support program

Abstract

To support the design of a regional service in consideration of the influence of the service on a region.SOLUTION: Storing evaluation index data for associating each of evaluation indexes for measuring a degree of achievement of a target with a value type measured by the evaluation index, region data for recording, for each of regions, effectiveness of each of applied services in the region and an evaluation index value of each of the evaluation indexes in the region, and service data for associating each of the services with a value type provided by the service, and grouping the regions into a plurality of maturity levels on the basis of the effectiveness of the service; On the basis of a maturity level to which each area belongs, each evaluation index value in each area, and a value type associated with each evaluation index, a combination of an area and a value type to be an object of a measure is specified, and for the combination, a recommended service recommended to the area is specified on the basis of the effectiveness of a service belonging to the value type.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a technology for supporting the design of services adapted to a region. [Background technology]

[0002] Patent Document 1 discloses a technology that quantifies the decisions made by people involved in urban design (urban stakeholders) using key performance indicators (KPIs). The urban lifecycle management system in Patent Document 1 evaluates interactions between urban objects and derives KPIs for urban areas according to the evaluated interactions.

[0003] In this context, urban objects include infrastructure and people in urban areas that are related to urban planning and sustainable development. Urban objects can also be considered to include various entities that affect the city's climate. Infrastructure objects include various buildings, such as residential, commercial, and public buildings. Infrastructure objects also include transportation, roads, and mobility. Infrastructure objects also include energy supply facilities and equipment, including energy generation and storage.

[0004] The system of Patent Document 1 enables simulation for urban development and management, and makes it possible to support decision-making regarding sustainable urban development. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Publication US2014 / 0188449A1 Summary of the Invention [Problem to be solved by the invention]

[0006] Each city has its own unique characteristics. Regions outside of cities also have various characteristics. Each region has its own set of problems that need to be addressed. Therefore, the effect obtained by applying a service as a policy to a certain region depends not only on the content of the service itself, but also on the characteristics of the region to which the service is applied. However, the system in Patent Document 1 does not take into account the influence of regional characteristics when evaluating the service.

[0007] One objective of the present disclosure is to provide a technique that enables support for the design of local services that take into account the impact of the services on the local area. [Means for solving the problem]

[0008] A local service design support system according to one aspect of the present disclosure includes a processing device and a storage device, The storage device stores evaluation index data that associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The processing device includes: grouping regions into a plurality of maturity levels based on the effectiveness of said services; Identifying combinations of regions and value types that should be targeted by measures based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, a recommended service to be recommended for the area is identified based on the effectiveness of the service belonging to the value type.

[0009] A local service design support method according to one aspect of the present disclosure is a local service design support method using a system having a processing device and a storage device, the method comprising: The storage device stores evaluation index data that associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The processing device grouping regions into a plurality of maturity levels based on the effectiveness of the service; A step of identifying a combination of a region and a value type to be targeted for a policy based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, identifying a recommended service to be recommended for the area based on the effectiveness of the service belonging to the value type; It has.

[0010] A local service design support program according to one aspect of the present disclosure is a local service design support program executable by a system having a processing device and a storage device, The storage device stores evaluation index data that associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The processing device includes: grouping regions into multiple maturity levels based on the effectiveness of said services; A function of identifying a combination of a region and a value type to be targeted for a policy based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type to which each evaluation index is associated; A function of identifying recommended services for the area based on the effectiveness of services belonging to the value type for the combination; Make this a reality. [Effects of the Invention]

[0011] According to one aspect of the present disclosure, it is possible to provide a regional service design support system, a regional service design support method, and a regional service design support program that enable support for the design of regional services that take into account the impact of the service on the region.

[0012] In particular, local stakeholders can easily observe the performance of their local area, compare it with other regions, see the latest data and identify the most important KPIs, and receive direct suggestions for services to implement. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram showing an example of a method for evaluating, comparing, visualizing, and proposing urban areas based on KPIs in a local service design support system device according to an embodiment of the present disclosure. [Figure 2] 10 is a flowchart illustrating a method for inputting an effectiveness for a given region according to an embodiment of the present disclosure. [Figure 3] 1 is a flowchart illustrating a method for automated collection, processing, and visualization of KPI data by region according to an embodiment of the present disclosure. [Figure 4] 1 is a flowchart illustrating an example of processing by a local service design support system according to an embodiment of the present disclosure. [Figure 5] FIG. 1 illustrates an example interface according to an embodiment of the present disclosure. [Figure 6] FIG. 1 is a diagram showing an example of the configuration of a KPI database according to an embodiment of the present disclosure. [Figure 7A] FIG. 2 is a diagram showing an example of the configuration of a region database according to an embodiment of the present disclosure. [Figure 7B] FIG. 2 is a diagram showing an example of the configuration of a region database according to an embodiment of the present disclosure. [Figure 8] FIG. 10 is a diagram showing an example of the configuration of a service database according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0015] FIG. 1 is a diagram showing an example of a KPI-based urban area evaluation, comparison, visualization, and proposal method in a local service design support system device according to an embodiment of the present disclosure.

[0016] The regional service design support system 1 comprises a regional design device 100 and a terminal device 150. The regional design device 100 comprises a program 110, a storage unit 120, a visualization unit 130, a processor 131, and an input / output device 132.

[0017] The program 110 includes a KPI data collection unit 111 and a data processing unit 112. The storage unit 120 stores a KPI database 121, a region database 122, and a service database 123.

[0018] The processor 131 loads and executes various programs to realize the functions of the KPI data collection unit 111, the data processing unit 112, and the visualization unit 130.

[0019] The KPI data collection unit 111 performs KPI data collection processing, which is processing for calculating or collecting KPI data.

[0020] The data processing unit 112 performs overall data processing.

[0021] The visualization unit 130 performs visualization processing. The visualization processing is processing for generating display data for the user based on information processed by the processor 131. Examples of the display data include, but are not limited to, regional data, comparison charts, and information showing service proposals to the user.

[0022] The input / output device 132 is, for example, a keyboard, mouse, touchpad, display monitor, screen, etc., and realizes the function of outputting information from the regional design device 100 to the user and inputting information from the user to the regional design device 100.

[0023] The regional design device 100 is communicably connected to an external device via a communication line 140 such as the Internet. An example of the external device is a terminal device 150. However, the external device is not limited to the terminal device 150.

[0024] (Example of processing performed by the processing device) The processor 131 corresponds to the processing device. The storage unit 120 and other storage areas included in the regional design device 100 correspond to the storage device. Below, an example of processing by the processing device based on data stored in the storage device will be shown.

[0025] The storage unit 120 stores evaluation index data that associates each evaluation index, which measures the degree of goal achievement, with a value type, which is the type of value measured by the evaluation index. The storage unit 120 stores area data that records, for each area, the effectiveness, which is the degree of success of each applied service in the area, and the evaluation index values, which are the values ​​of each evaluation index in the area. The storage unit 120 stores service data that associates each service with a value type, which is the type of value provided by the service. The evaluation index data corresponds to a KPI database 121, which will be described later with reference to FIG. 6. The area data corresponds to an area database 122, which will be described later with reference to FIG. 7. The service data corresponds to a service database 123, which will be described later with reference to FIG. 8.

[0026] The processor 131 groups regions into multiple maturity levels based on the effectiveness of services. The processor 131 identifies combinations of regions and value types to be targeted by measures based on the maturity level to which each region belongs, the evaluation index values ​​for each region, and the value types associated with each evaluation index. The processor 131 identifies recommended services to be recommended for the region based on the effectiveness of services belonging to the value type for each combination.

[0027] The effectiveness of the service will be discussed later. Evaluation indicators are indicators that show the degree of achievement of goals, such as KPIs, in that region. The maturity level is a number that shows the level of maturity (achievement) of that region in terms of evaluation indicators. Value types refer to the categories that will be discussed later.

[0028] The processor 131 calculates, for each maturity level and for each value type, the average value of the evaluation index values ​​belonging to that value type as the cluster evaluation index average value. For each region and for each value type, the processor 131 calculates, for each region and for each value type, the average value of the evaluation index values ​​belonging to that value type as the region evaluation index average value. For each region and for each value type, the processor 131 calculates a difference value by subtracting the cluster evaluation index average value of the maturity level to which the region belongs from the region evaluation index average value of the region. The processor 131 normalizes the difference values ​​of each value type in each region using the same standard to make them comparable to each other and calculates a normalized difference value. The processor identifies the combination of region and value type with the smallest normalized difference value.

[0029] For a combination of an area to be targeted by a policy and a value type, the processor 131 identifies, as a recommended service, the most effective service among services belonging to the value type in the area.

[0030] Processor 131 calculates the number k of maturity levels into which regions are grouped by silhouette analysis based on the effectiveness of each service in each region, and groups the regions into k maturity levels by k-means clustering based on the effectiveness of the services.

[0031] The processor 131 recalculates the effectiveness of the recommended service for the area and the evaluation index value related to the recommended service for the area when the recommended service is applied to the area that is the combination of the area and value type that should be the target of the policy, and again groups each area into maturity levels.

[0032] The processor 131 displays an image that visualizes the magnitude of the evaluation index values ​​in a plurality of regions so that they can be compared.

[0033] The evaluation index data further defines, for each evaluation index, a possible numerical range, which is the range within which the value of the evaluation index can take on. The possible numerical range corresponds to possible numerical range 204 in Figure 6. Processor 131 acquires the value of each evaluation index in each region, normalizes the value based on the possible numerical range, and records the value in the region data as an evaluation index value.

[0034] The processor 131 prompts the user to input the effectiveness of the service in each of the regions, and records the input effectiveness value in the region data.

[0035] FIG. 2 is a flowchart illustrating a method for inputting an availability for a given region according to an embodiment of the present disclosure.

[0036] This flowchart shows the flow for manually initializing the availability of local services in the database. The purpose of this flow is to allow the user to input the availability of services in a certain category that are in the user's area.

[0037] An area list, which is data related to an area, is extracted from the area database 122 and displayed to the user via the visualization unit 130 (S200). A service list, which is information indicating services provided in the area, is extracted from the service database 123 and displayed to the user via the visualization unit 130 (S201).

[0038] In steps S202 and S203, the user selects his / her region, a KPI or service category, and services belonging to that category, if any, based on the information displayed on the visualization unit 130, and inputs the effectiveness corresponding to the combination of the above selections. Note that if the service for which the effectiveness is to be input is not present in the displayed service list, the user may add the service to the list together with its effectiveness via the input / output device 132.

[0039] The inputted data of the effectiveness is stored in the service database 123 in association with the service content (S204). The inputted data of the effectiveness is stored in the area database 122 in association with the area (S205).

[0040] FIG. 3 is a flowchart illustrating a method for automated collection, processing, and visualization of KPI data by region according to an embodiment of the present disclosure.

[0041] After the processing of the flowchart in Fig. 2 is performed, the processing shown in the flowchart in Fig. 3 is performed. The KPI data collector 111 obtains KPI list information from the KPI database 121 (S301). The KPI list information includes each data source, if any, and the acceptable score range for the KPI. The score range is a predefined range used to compare with the actual local KPI value to calculate the final KPI score. The comparison process with the KPI value will be described later.

[0042] The KPI data collection unit 111 accesses the KPI database 121 for data mining and accesses other available data such as survey responses by region in the KPI database 121 (S302).

[0043] The data processing unit 112 performs some pre-processing operations on the collected data, as in step S303. The raw KPI data has different units and / or different scales as collected. As an example, consider the following two KPIs: -Employment rate (example data value: 75%. The higher the value, the more ideal it is) -Carbon emissions (example data value: 15Mt CO2 equivalent, low values ​​are ideal)

[0044] In the above example, the two KPIs have different units and different "directions" (lower is better, higher is better). To make the KPI data usable for comparison, the data must be standardized. For example, the method is as follows:

[0045] For each KPI, we calculate a normalized score based on how well a city performs within the acceptable range using the following formula:

[0046] -Higher is better KPIs: (Normalized score) = (((actual score) - (minimum acceptable score)) / ((maximum acceptable score) - (minimum acceptable score))) x 100

[0047] -For KPIs where the lower the better: (Normalized score) = (((Maximum acceptable score) - (Actual score)) / ((Maximum acceptable score) - (Minimum acceptable score))) x 100

[0048] These formulas ensure that for KPIs where higher scores are better, a higher normalized score indicates better performance, and for KPIs where lower scores are better, a lower normalized score indicates better performance (the lower the actual score, the higher the normalized score). A weighted score is then calculated. Assuming equal weights (all KPIs are equally important), the normalized score is multiplied by 1 / (number of KPIs).

[0049] Next, as shown in steps S304 and S305, the results of each KPI score are stored in the KPI database 121 and the region database 122. The visualization unit 130 can present the user with a graph, such as a radar chart, showing all KPI normalized values ​​for the selected region (S306).

[0050] In the background, the KPI data collector 111 periodically checks for changes in data from the sources and sends them to the data processor 112 for reprocessing (S307), and similarly, data processing operations are performed and the visualization is updated accordingly. That is, the processing of steps S308 to S311 is performed in the same way as the processing of steps S303 to S306.

[0051] 4 is a flowchart illustrating an example of processing by the local service design support system according to an embodiment of the present disclosure. The flowchart illustrates the main processes executed by the system, namely, an example of processing for determining maturity levels and allocating regions to the maturity levels, and an example of processing for service proposals.

[0052] The data processing unit 112 acquires the normalized KPI data scores of all regions in the database (S401). The data processing unit 112 forms clusters based on the similarity of the KPI data, and groups the regions into multiple levels according to the clusters (S402).

[0053] In the embodiment shown in the present disclosure, the number of levels and their thresholds can be set arbitrarily. In another embodiment, for example, as shown in Figure 4, K-Means clustering is used. This allows regions to be automatically grouped into multiple levels based on KPI scores, so there is no need to predefine levels or thresholds. Then, the following steps are performed:

[0054] 1. Deciding the number of clusters (K) There are various methods for determining the optimal value of k, such as Silhouette Analysis and the Elbow Method.

[0055] As an example, we will show how to use silhouette analysis. -Calculate silhouette scores for various K values ​​(e.g., 2-10). - Silhouette scores range from -1 to 1, with values ​​closer to 1 indicating good cluster separation and values ​​closer to -1 indicating poor separation. - Choose the K value that corresponds to the highest average silhouette score. This K value indicates the optimal number of clusters that best captures the natural groupings in the data.

[0056] Here, we assume that the highest average silhouette score is achieved when K=4, which indicates that grouping cities into four clusters based on their KPI scores is optimal.

[0057] 2. Applying K-means clustering -Apply K-Means clustering using the determined K value.

[0058] The algorithm iteratively assigns cities to clusters based on their normalized scores, minimizing the sum of the squared distances between each city and its cluster centroid (averaged across all KPIs for that cluster).

[0059] Applying K-Means with K=4, for example, if there are 15 urban areas, the 15 cities will be grouped into 4 clusters, and each city will belong to the cluster with the closest centroid (average of normalized scores across all KPIs).

[0060] 3. Cluster analysis -Analyze the resulting clusters and their centroids. -Compare the overall normalized score mean (average normalized score across all KPIs) within each cluster.

[0061] In this way, the optimal number of clusters is automatically determined and cities are assigned to clusters based on the similarity of their KPI scores. This eliminates user subjectivity in selecting the number of levels and setting thresholds, and provides a data-driven approach to grouping cities. This method also recalculates the optimal number of clusters (K) based on new data sizes, allowing it to handle a variety of city counts and KPIs.

[0062] The level subdivision results associated with each area are stored in the region database 122 according to step S403, and the visualization for the user changes accordingly (S404). For example, a function to compare areas within the same level may be added.

[0063] The second important part of the flow starts from step S405, from which the data processing unit 112 performs further calculations aimed at proposing candidate services to be implemented in the required areas. A detailed description of this method follows.

[0064] As mentioned above, each region introduces different services for each category (environment, economy, etc.) that also contributes to the region's KPIs, and each service has an effectiveness score. The effectiveness score indicates how much the service contributes to the welfare of citizens and how well it is accepted. The effectiveness score is an arbitrary number. For example, the effectiveness score ranges from 1 to 5, with 1 being highly positive and 5 being low and negative.

[0065] For example, Region 9 may offer the following services for each category with their associated effectiveness:

[0066] -Inclusiveness: {Digital accessibility services (2), online citizen participation platform (1), multilingual information services (2)} -Environment: {Smart waste management services (2), air quality monitoring networks (1), green space mapping applications (3)} -Economy: {Local business support platform (2), smart payment solution for the market (1), tourism campaign generator platform (1), entrepreneurship development network (1)}

[0067] Similarly, other areas may provide other services for these categories and other categories such as welfare and safety.

[0068] As follows, in one example embodiment, the following method is provided to suggest candidate services to each city to implement by observing the results of KPI scores, the level to determine key areas to improve, and the effectiveness of categories of areas that are performing well:

[0069] 1. Identify key areas for improvement: Calculate the average normalized KPI scores for regions in each category.

[0070] 2. For all regions, calculate the difference between the region's average KPI score for each category and the corresponding average category score for the cluster to which the region belongs.

[0071] 3. Normalize the difference for each category across all regions.

[0072] 4. Identify categories where the absolute value of the normalized difference is negative and high, representing areas that need the most improvement.

[0073] 5. For each identified category within a region, give high priority to the candidate services with the highest effectiveness within that category and region.

[0074] The results (identified proposed candidate services, identified improvement categories) are stored in the regional database 122 and presented to the user through the visualization unit 130 in steps S406, S407, and S408.

[0075] This allows users, who are city stakeholders, reviewers or decision makers, to make decisions based on the data displayed and the proposals received by the system.

[0076] As can be seen in steps S410 and S411, the local service design support system 1 constantly checks for data updates, so that when changes are identified in the data and calculations result in a reorganization of levels or a reallocation of areas to levels, the changes are reflected in the system and visualized to present the effectiveness of the implementation of the new service.

[0077] 5 illustrates an example interface according to an embodiment of the present disclosure. The interface of an embodiment of the present disclosure includes a graph for displaying results and comparing different regions, and an input function for updating the user's region. As shown, the user can select regions to compare and make comparisons based on regional KPIs. The user can update or add services and their associated effectiveness for their region.

[0078] 6 is a diagram illustrating an example configuration of a KPI database according to an embodiment of the present disclosure. The KPI database 121 includes, as information items, a list of predefined examples of KPIs and their associated categories. The KPI database 121 further includes, as information items, a possible numerical range, which is a predefined acceptable score range, and an indicator type, which indicates whether a higher score is better (positive indicator) or a lower score is better (negative indicator).

[0079] 7A and 7B are diagrams illustrating an example of the configuration of a regional database according to an embodiment of the present disclosure. The regional database 122 is shown in two figures, each showing information items in a single table in the regional database 122, due to lack of space and simplification of the drawings in this specification.

[0080] The table stored in the region database 122 stores the region ID and region name together with the maturity level assigned after calculation by the data processing unit 112 .

[0081] Since the list of KPIs is a formally prepared list (which may or may not change over time, in which case the database is updated accordingly), each KPI (304 to 313) is an information item in a table stored in the regional database 122, where the KPI data collected for each region is stored.

[0082] The table stored in the region database 122 also includes the service availability for each region, as shown in information items 314 to 316.

[0083] 8 is a diagram illustrating an example of the configuration of a service database according to an embodiment of the present disclosure. The service database 123 shows a list of services in the same category as the KPI and their related categories. As shown in columns 504 to 506, the service database 123 includes, as information items, effectiveness levels collected from experts in each area.

[0084] The above-described embodiments are merely illustrative examples of the present invention, and are not intended to limit the scope of the present invention to these embodiments alone. Those skilled in the art can implement the present invention in various other forms without departing from the scope of the present invention.

[0085] The above-described embodiment includes the following features, but the features included in the embodiment are not limited to the following features.

[0086] (Item 1) The regional service design support system has a processing device and a storage device, The storage device stores evaluation index data that associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The processing device includes: grouping regions into a plurality of maturity levels based on the effectiveness of said services; Identifying combinations of regions and value types that should be targeted by measures based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, a recommended service to be recommended for the area is identified based on the effectiveness of the service belonging to the value type. This allows regions to be classified into multiple maturity levels, and recommended services are identified by taking into account the evaluation index values ​​for each region along with the maturity level to which each region belongs, making it possible to support the design of regional services that take into account the impact of the services on the region.

[0087] In the local service design support system according to item 1, The processing device includes: For each maturity level, for each value type, the average value of the evaluation index values ​​belonging to that value type is calculated as the cluster evaluation index average value; For each region, for each value type, the average value of the evaluation index values ​​belonging to that value type is calculated as the regional evaluation index average value, For each region, for each value type, a difference value is calculated by subtracting the average value of the cluster evaluation index of the maturity level to which the region belongs from the average value of the region evaluation index of the region; The difference values ​​of each value type in each region are normalized to be comparable with each other based on the same standard to calculate the normalized difference value, The combination of the area and value type that has the smallest normalized difference value is identified. This makes it possible to identify the region that corresponds to the smallest average regional evaluation index value, which is the furthest from the average evaluation index value of regions belonging to a certain maturity level, as the region that should be the target of measures.

[0088] (Item 3) In the local service design support system according to item 1, The processing device identifies, for the combination, the service with the highest effectiveness among services belonging to the value type in the area as the recommended service. This makes it possible to recommend the most effective services to areas that should be targeted by measures.

[0089] (Item 4) In the local service design support system according to item 1, The processing device includes: Calculate the number k of maturity levels for grouping regions by silhouette analysis based on the effectiveness of each service in each region; Regions are grouped into k maturity levels using k-means clustering based on service effectiveness. This allows for automatic grouping of regions into k maturity levels based on silhouette analysis.

[0090] (Item 5) In the local service design support system according to item 4, The processing device includes: recalculating the effectiveness of the recommended service for the region and the evaluation index value related to the recommended service for the region when the recommended service is applied to the region of the combination; The grouping of each region into maturity levels is carried out again. This makes it possible to measure the change in the relative maturity level of each region after applying the recommended service to the region where the policy is recommended, thereby supporting the design of continuous regional services.Furthermore, by using this in simulations, it is possible to predict the change in the relative maturity level of each region when the recommended service is applied to the region where the policy is recommended, thereby supporting the design of optimal regional services that take into account the overall balance of each region.

[0091] (Item 6) In the local service design support system according to item 1, The processing device displays an image that visualizes the magnitude of the evaluation index values ​​in a plurality of regions so that they can be compared. This makes it possible to support the design of regional services by comparing the evaluation index values ​​of multiple regions.

[0092] (Item 7) In the local service design support system according to item 1, The evaluation index data further defines, for each evaluation index, a possible numerical range that is a range that the value of the evaluation index can take, The processing device acquires the value of each evaluation index in each of the regions, normalizes the value based on the possible numerical range, and records the value as the evaluation index value in the region data. This allows evaluation index values ​​that are normalized based on the possible numerical range of the evaluation index and are easier to use for comparisons and the like to be recorded in the regional data.

[0093] (Item 8) In the local service design support system according to item 1, The processing device prompts the user to input the effectiveness of the service in each of the regions, and records the input effectiveness value in the region data. This allows the user to evaluate the effectiveness of the service and record the evaluation details as area data.

[0094] (Item 9) A method for supporting local service design using a system having a processing device and a storage device, comprising: The storage device stores evaluation index data that associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The local service design support method includes: The processing device grouping regions into a plurality of maturity levels based on the effectiveness of said services; Identifying combinations of regions and value types that should be targeted by measures based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, a recommended service to be recommended for the area is identified based on the effectiveness of the service belonging to the value type. This allows regions to be classified into multiple maturity levels, and recommended services are identified by taking into account the evaluation index values ​​for each region along with the maturity level to which each region belongs, making it possible to support the design of regional services that take into account the impact of the services on the region.

[0095] (Item 10) A local service design support program executable by a processing device in a system having a processing device and a storage device, evaluation index data in which the storage device associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The local service design support program is The processing device includes: grouping regions into a plurality of maturity levels based on the effectiveness of said services; Identifying combinations of regions and value types that should be targeted by measures based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, a recommended service to be recommended for the area is identified based on the effectiveness of the service belonging to the value type. Make it happen. This allows regions to be classified into multiple maturity levels, and recommended services are identified by taking into account the evaluation index values ​​for each region along with the maturity level to which each region belongs, making it possible to support the design of regional services that take into account the impact of the services on the region. [Explanation of symbols]

[0096] 1...Regional service design support system, 100...Regional design device, 110...Program, 111...KPI data collection unit, 112...Data processing unit, 120...Memory unit, 121...KPI database, 122...Regional database, 123...Service database, 130...Visualization unit, 131...Processor, 132...Input / output device, 140...Communication line, 150...Terminal device

Claims

1. a processor and a memory device; The storage device stores evaluation index data that associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The processing device includes: grouping regions into a plurality of maturity levels based on the effectiveness of said services; Identifying combinations of regions and value types that should be targeted by measures based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, a recommended service to be recommended for the area is identified based on the effectiveness of the service belonging to the value type. Regional service design support system.

2. The processing device includes: For each maturity level, for each value type, the average value of the evaluation index values ​​belonging to that value type is calculated as the cluster evaluation index average value; For each region, for each value type, the average value of the evaluation index values ​​belonging to that value type is calculated as the regional evaluation index average value, For each region, for each value type, a difference value is calculated by subtracting the average value of the cluster evaluation index of the maturity level to which the region belongs from the average value of the region evaluation index of the region; The difference values ​​of each value type in each region are normalized to be comparable with each other based on the same standard to calculate the normalized difference value, Identifying the combination of area and value type that has the smallest normalized difference value; The local service design support system according to claim 1.

3. 2. The local service design support system according to claim 1, wherein said processing device identifies, for said combination, the most effective service among services belonging to said value type in said local area as said recommended service.

4. The processing device includes: Calculate the number k of maturity levels for grouping regions by silhouette analysis based on the effectiveness of each service in each region; Grouping regions into k maturity levels by k-means clustering based on service effectiveness; The local service design support system according to claim 1.

5. The processing device includes: recalculating the effectiveness of the recommended service for the region and the evaluation index value related to the recommended service for the region when the recommended service is applied to the region of the combination; Regrouping regions into maturity levels; 5. The local service design support system according to claim 4.

6. 2. The regional service design support system according to claim 1, wherein said processing device displays an image visualizing the magnitude of evaluation index values ​​in a plurality of regions so that the magnitudes of the evaluation index values ​​in the plurality of regions can be compared.

7. The evaluation index data further defines, for each evaluation index, a possible numerical range that is a range that the value of the evaluation index can take, the processing device acquires the values ​​of the evaluation indexes in each of the regions, normalizes the values ​​based on the possible numerical ranges, and records the normalized values ​​as the evaluation index values ​​in the region data; The local service design support system according to claim 1.

8. The processing device prompts the user to input the effectiveness of the service in each of the regions, and records the input effectiveness value in the region data. The local service design support system according to claim 1.

9. A method for supporting local service design using a system having a processing device and a storage device, comprising: The storage device stores evaluation index data that associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The processing device grouping regions into a plurality of maturity levels based on the effectiveness of said services; Identifying combinations of regions and value types that should be targeted by measures based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, a recommended service to be recommended for the area is identified based on the effectiveness of the service belonging to the value type. Methods for supporting the design of local services.

10. A local service design support program executable by a processing device in a system having a processing device and a storage device, evaluation index data in which the storage device associates each evaluation index that measures the degree of achievement of a goal with a value type that is a type of value measured by the evaluation index; Regional data recording, for each region, the effectiveness of each applied service, which is the degree of success in that region, and the evaluation index value, which is the value of each evaluation index in that region; service data associating each service with a value type that is a type of value provided by the service; The regional service design support program The processing device includes: grouping regions into a plurality of maturity levels based on the effectiveness of said services; Identifying combinations of regions and value types that should be targeted by measures based on the maturity level to which each region belongs, the value of each evaluation index in each region, and the value type associated with each evaluation index; For the combination, a recommended service to be recommended for the area is identified based on the effectiveness of the service belonging to the value type. A local service design support program that helps you put this into practice.

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