Service proposal support device, service proposal support method, and service proposal support system

Abstract

To appropriately select maintenance services to be proposed to a customer, taking into account a risk such as maintenance staff resource shortages faced by a maintenance provider.SOLUTION: A service proposal support device according to the present invention comprises: a reception part that receives service target equipment information and maintenance provider information including information regarding a maintenance staff resource; a scenario creation part that creates multiple service scenarios with different service parameter values based on the received information; a scenario evaluation value calculation part that calculates a scenario evaluation value for each service scenario, including at least the resource tightness level of a maintenance provider; a scenario selection part that selects a service scenario where the scenario evaluation value satisfies predetermined conditions; and a display part that displays the service scenario satisfying the predetermined condition.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a service proposal support device, a service proposal support method, and a service proposal support system, and more particularly to a service proposal support device, a service proposal support method, and a service proposal support system that support the proposal of maintenance services for electric power facilities and the like. [Background technology]

[0002] In recent years, facilities (assets) in the electric power, railway, infrastructure, industrial plants, etc. have been aging, increasing the risk of breakdowns. To ensure the safe and stable operation of these facilities, it is necessary to provide appropriate and continuous maintenance services, such as inspections, condition monitoring, repairs, and part replacement. In particular, in the electric power sector, the need for a safe and stable power supply with high availability is growing amid the diversification of assets, including the construction and renewal of facilities due to the increase in aging and obsolete facilities, and the increase in renewable energy. For maintenance companies that undertake maintenance of a wide variety of customer facilities and provide maintenance services, this presents an opportunity to expand their maintenance services with new digital solutions utilizing sensors and IoT technology.

[0003] In this situation, a solution proposal support system that supports proposals to maintenance service customers is described in Patent Document 1. The solution proposal support system in Patent Document 1 references a problem-solution information table that stores problems and solutions in association with each other, extracts solutions that correspond to problems that have a high degree of similarity with search keywords that indicate the user's problem, and calculates an index of the introduction effect of the solution. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2023-135499 Summary of the Invention [Problem to be solved by the invention]

[0005] While the need for maintenance services is increasing, there is also the issue of labor shortages, and maintenance companies need to expand new maintenance services with limited maintenance personnel resources while maintaining the quality of existing maintenance services.If new maintenance services are expanded indiscriminately in response to increasing needs, there is a risk that maintenance companies will run out of maintenance personnel resources and will not be able to quickly provide existing maintenance services such as inspections and repairs.

[0006] In addition, the provision of digital solutions such as predictive detection using IoT technology is expected to prevent failures before they occur and reduce the need to respond to sudden failures. On the other hand, if detection performance is insufficient, there will be an increase in false alarms and missed alerts, which will create a new risk of increasing the number of maintenance personnel having to respond.

[0007] Patent Document 1 describes a method for proposing solutions using the effectiveness of the solution in resolving customer issues as an indicator. However, Patent Document 1 does not mention a mechanism for considering risks such as a shortage of maintenance personnel resources at the maintenance company that proposes the solution. An object of the present invention is to appropriately select a maintenance service to be proposed to a customer, taking into consideration risks to the maintenance company, such as a shortage of maintenance personnel resources. [Means for solving the problem]

[0008] The service proposal support device of the present invention is characterized by comprising: a reception unit that receives information on equipment to be serviced and maintenance company information including information on maintenance personnel resources; a scenario creation unit that creates multiple service scenarios with different service parameter values ​​based on the received information; a scenario evaluation value calculation unit that calculates a scenario evaluation value for each service scenario that includes at least the resource tightness of the maintenance company; a scenario selection unit that selects a service scenario whose scenario evaluation value satisfies specified conditions; and a display unit that displays the service scenarios that satisfy the specified conditions. Other means will be described in the detailed description of the invention. [Effects of the Invention]

[0009] According to the present invention, it is possible to appropriately select maintenance services to be proposed to customers, taking into consideration risks to the maintenance company, such as a shortage of maintenance personnel resources. [Brief explanation of the drawings]

[0010] [Figure 1] This is an image of the business model of a maintenance company. [Figure 2] 2 illustrates an example of a hardware configuration of a service proposal support device according to a first embodiment. [Figure 3] 1 is a functional block diagram of a service proposal support device according to a first embodiment. [Figure 4] 10 is an example of service target facility information. [Figure 5] 10 is an example of maintenance task information included in maintenance operator information. [Figure 6] This is an example of organization and maintenance worker information among maintenance business operator information. [Figure 7] 10 is an example of service scenario information. [Figure 8] 10 is an example of an output screen of a calculation result of a scenario evaluation value. [Figure 9] 10 is an example of an output screen of a calculation result of a scenario evaluation value. [Figure 10] 10 is a flowchart of a processing procedure. [Figure 11] FIG. 10 is a functional block diagram of a service proposal support device according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, first and second embodiments of the present invention will be described with reference to the drawings. Note that assets that are the subject of maintenance services include various items such as facilities, equipment, machinery, and products.

[0012] Example 1 FIG. 1 is an image diagram of the business model of a maintenance business operator 1. The maintenance business operator 1 provides maintenance services for a wide variety of equipment for multiple customers A to G. The maintenance services cover a wide range of areas, including, for example, the provision of maintenance work services such as repairs, inspections, parts replacement, and equipment renewal, the provision of digital solutions using IoT such as condition monitoring and predictive diagnosis, and the conclusion of contracts that guarantee the uptime of customer equipment. The service proposal support device assists the maintenance business operator 1 in considering what kind of maintenance services (scale, performance, etc.) should be provided to which customers.

[0013] (term) First, for ease of understanding, definitions of main terms will be explained. The following definitions are common to Example 1 and Example 2. A maintenance service is a service that a maintenance company 1 provides to a customer's assets (equipment, etc.) with the aim of ensuring that the assets of the customer of the maintenance company 1 function properly. A maintenance service is also simply called a "service."

[0014] Service parameters are items that constitute a maintenance contract concluded between the maintenance business operator 1 and a customer, such as the target equipment site, the number of target equipment units, the service menu, and service performance. Each service parameter can take on at least one of a plurality of service parameter values ​​(service parameter values) that are prepared in advance. The service parameter values ​​are, for example, "regular inspection," "fault response," "condition monitoring," "predictive maintenance," and the like for the "service menu" as a service parameter.

[0015] A service scenario is a combination of multiple service parameter values ​​selected for each of the service parameters. Therefore, a service scenario is the content of a maintenance contract itself. In many cases, a service scenario is a candidate for the content of a future maintenance contract, and is the subject of a simulation in which the service parameter values ​​are varied in various ways. The term "scenario" is implicit in its meaning. A "service scenario" is also simply called a "scenario."

[0016] The scenario evaluation is an evaluation of each of the multiple candidate service scenarios, which is carried out to quantitatively compare the multiple candidate service scenarios. The scenario evaluation value is a value calculated for each service scenario in the scenario evaluation. Examples of scenario evaluation values ​​include the "average availability rate of maintenance personnel" and the "availability rate of equipment covered by existing contracts," which will be described later.

[0017] (Hardware configuration) 2 shows an example of the hardware configuration of the service proposal support device 100. The service proposal support device 100 is a general computer, and includes a central control unit 11, input devices 12 such as a camera, microphone, mouse, and keyboard, output devices 13 such as a speaker and display, a main memory device 14, an auxiliary memory device 15, and a communication device 16.

[0018] The auxiliary storage device 15 stores service target facility information 1001, maintenance company information 1002, service parameters 1003, and service scenario information 1004 (not shown in FIG. 2 , but described in detail later). The auxiliary storage device 15 may be configured as a separate housing independent of the service proposal support device 100. These configurations are common to the first and second embodiments. A facility 31 and another system 32 are connected to the service proposal support device 100 via a network 21.

[0019] The service proposal support device 100 can acquire operation information and the like in real time from the facility 31 and perform status monitoring and the like. The service proposal support device 100 and the facility 31 constitute a service proposal support system. The service proposal support device 100 can also acquire service target facility information 1001, maintenance company information 1002, service parameters 1003, and service scenario information 1004 from another system 32.

[0020] 3 is a functional block diagram of a service proposal support device 100 according to the first embodiment. The service proposal support device 100 of the first embodiment includes a reception unit 101, a scenario creation unit 102, a scenario evaluation value calculation unit 103, a scenario selection unit 104, and a display unit 105, which are programs. In the following description, when a subject is described as "XX unit," it means that the central control device 11 reads the program from the auxiliary storage device 15 to the main storage device 14 and realizes the function of the program.

[0021] The reception unit 101 receives service target facility information 1001 and maintenance business operator information 1002. These pieces of information are information relating to the maintenance service business currently being provided by the maintenance business operator. Although we are in the middle of Fig. 3, the explanation will now move on to Figs. 4 to 6.

[0022] 4 is an example of the service target facility information 1001. The service target facility information 1001 stores the following information in mutual association.

[0023] The asset ID in the asset ID column 111 is an identifier that uniquely identifies the asset of the customer. The asset ID includes a character string that indicates the type of asset at a glance, such as "windmill." The site ID in the site ID column 112 is an identifier that uniquely identifies the site (pinpoint location) where the asset is located. The area ID in the area ID column 113 is an identifier that uniquely identifies the area to which the site where the asset is located belongs (the "preservation project target area" in FIG. 1).

[0024] The connected asset in the connected asset column 114 is the asset ID of another asset that is directly connected to the asset. The model in the model column 115 is the type of asset (model number, etc.). The age in the age field 116 is the number of years that have passed since the asset was produced. The health index in the health index column 117 is an index that indicates the health of the asset. The smaller the health index, the higher the health. The health index gradually increases during a period when no maintenance service is received, and immediately after maintenance service, it drops to a small value (for example, "0"). The travel time in the travel time column 118 is the time required for the maintenance personnel to travel from the base of the maintenance company to the site where the asset is located.

[0025] 5 and 6 are examples of the maintenance company information 1002. The maintenance company information 1002 includes maintenance task information 1002a (FIG. 5) and organization and maintenance staff information 1002b (FIG. 6). It can be said that the maintenance company information 1002 includes "information related to maintenance staff resources."

[0026] 5 is an example of the maintenance task information 1002a included in the maintenance company information 1002. The maintenance task information 1002a stores the following information in association with each other.

[0027] The model in the model column 121 is the same as the model in FIG. 4, but here it is particularly the type (model number, etc.) of the asset that is the target of the task (described immediately below). The task ID in the task ID column 122 is an identifier that uniquely identifies a task. A task is a unit of work that constitutes a maintenance service. A task can also be said to be a combination of specific values ​​for the model, task type, required skills, required number of people, standard work time, whether or not there is a power outage, implementation trigger, and work cost.

[0028] The task type in the task type column 123 is a category of the task, and in this case is either "inspection," "periodic maintenance," "preventive maintenance," or "corrective maintenance." The required skills in the required skills column 124 are the techniques, abilities, or qualifications required of the maintenance personnel who will perform the task. The required number of personnel in the required number of personnel column 125 is the number of maintenance personnel required for the task. The standard work time in the standard work time column 126 is the work time required from the start to the end of a task.

[0029] The power outage column 127 indicates whether or not there will be a power outage, and is either "Yes" indicating that the asset must be powered down when performing the task, or "No" indicating that there is no need for a power outage. The power outage time is equal to the standard work time. The execution trigger in the execution trigger column 128 is an event that triggers the start of a task. For example, "Cycle = 90 days" means that the task is repeated every 90 days. "Health index > 50" means that the task is performed when the health index exceeds 50. The work cost in the work cost column 129 is the cost required for one task (excluding labor costs).

[0030] 6 is an example of organization and maintenance staff information 1002b included in the maintenance company information 1002. The organization and maintenance staff information 1002b stores the following information in mutual association.

[0031] The maintenance personnel ID in the maintenance personnel ID column 131 is an identifier that uniquely identifies a maintenance personnel. The maintenance personnel belongs to the maintenance company 1 and is responsible for the tasks. In addition, in the first and second embodiments, the maintenance personnel is synonymous with the "maintenance personnel resource" and the "resource." The skills in the skill column 132 are the techniques, abilities, or qualifications possessed by the maintenance personnel. The base ID in the base ID column 133 is an identifier that uniquely identifies a base. A base is the location of the organization to which the maintenance technician belongs. The area in the area of ​​responsibility column 134 is the area ID of the area that the maintenance technician is responsible for. The shift start time in the shift start time column 135 is the time when the maintenance worker starts the task. The work end time in the work end time column 136 is the time when the maintenance worker ends the task. The holidays in the holiday column 137 are the days of the week on which maintenance personnel are allowed to rest. The labor costs in the labor cost column 138 are the amount of salary paid to maintenance personnel. For the explanation, refer back to FIG.

[0032] The receiving unit 101 receives the above-mentioned various information (service target facility information 1001, maintenance company information 1002, and service parameters 1003) input by the user to the input device 12 of the service proposal support device 100. In addition, the reception unit 101 can receive various information from other systems 32 (Figure 2) such as an enterprise asset management system, an asset performance monitoring system, a condition monitoring system, and a field service management system.

[0033] In this way, by linking with the other system 32, the user can save time and effort in inputting information, and the service proposal support device 100 can be operated while updating the latest information on the facilities as needed. Also, various types of information may be stored as a database in the auxiliary storage device 15 of the service proposal support device 100.

[0034] The scenario creation unit 102 creates a service scenario. As described above, a service scenario defines the contents of a maintenance contract that a maintenance company concludes with a customer. Based on the various information received by the reception unit 101, the scenario creation unit 102 creates a scenario for the maintenance service that the maintenance company is currently providing. In addition to the current contract contents, the scenario creation unit 102 uses the service parameters 1003 to create future service scenarios with multiple different contract contents. Although we are still in the middle of Figure 3, the explanation will now move on to Figure 7.

[0035] 7 is an example of the service scenario information 1004. The service scenario information 1004 stores the following information in association with each other.

[0036] The scenario ID in the scenario ID column 141 is an identifier that uniquely identifies a scenario. The "current situation" indicates the current contract details. The "current situation" differs from "scenarios," which are candidates for future maintenance contracts, in the sense that the details of the maintenance contract have already been decided. However, since it can be compared with future scenarios, it is treated here in the same category as scenarios. The customer category of the customer category 142 is either "existing" which indicates that the customer is an existing customer, or "new" which indicates that the customer is a potential customer. The target facility site in the facility target site column 143 is the site ID of the site where the asset that is the target of the service is located. The number of target facilities in the target facility number column 144 is the number of assets that are the target of the service.

[0037] The service menu in the service menu column 145 is the content of a specific service, and is the task type in FIG. 5 or a combination thereof. The service performance in the service performance column 146 is the quality of the service menu, and in many cases, is the accuracy of detecting asset failures in advance, the probability of asset failure, and the like. The contract conditions in the contract conditions column 147 are the contents that the maintenance business operator has specifically promised to the customer.

[0038] (Relationship between service parameters and service scenario information) As described above, the service parameters 1003 are items in the maintenance contract. In Fig. 7, the service parameters are the customer category, the target facility site, the number of target facilities, the service menu, the service performance, the contract conditions, etc.

[0039] Each service parameter is associated with multiple service parameter values. For example, the service parameter "service menu" is associated with multiple service parameter values ​​such as "regular inspections and failure response," "condition monitoring and predictive maintenance," "parts replacement and equipment renewal," etc. The service parameter "service performance" is associated with multiple service parameter values ​​such as "detection of failures after 90 days with 90% accuracy," "detection of failures after 10 days with 75% accuracy," "equipment failure probability reduced by half," etc.

[0040] The scenario creation unit 102 selects one service parameter value for each service parameter. The scenario creation unit 102 creates multiple service scenarios by combining the selected service parameter values. For example, if there are three service parameters and four service parameter values ​​are prepared for each of the service parameters, the scenario creation unit 102 can theoretically create four service scenarios. 3 =64 service scenarios can be created.

[0041] The plurality of service scenarios created in this way are accumulated as records to form service scenario information 1004. In other words, scenario creation unit 102 creates service scenarios with a plurality of different contract contents by varying the values ​​of a plurality of service parameters included in service parameters 1003 within a predetermined range. Return to FIG. 3 for an explanation.

[0042] The scenario evaluation value calculation unit 103 performs scenario evaluation by calculating a scenario evaluation value for evaluating the merits and demerits of each of the multiple service scenarios created by the scenario creation unit 102. One specific method for calculating a scenario evaluation value is agent simulation.

[0043] (Agent Simulation) The scenario evaluation value calculation unit 103 creates a model in the simulation world that corresponds to each asset (or asset component) in the real world. In the simulation world, this model operates autonomously and fails, thereby reproducing the behavior of the asset in the real world. This model is called an asset agent. Based on the service target facility information 1001, the scenario evaluation value calculation unit 103 can create an asset agent for each facility for which the maintenance operator provides service.

[0044] Similarly, the scenario evaluation value calculation unit 103 creates a model in the simulation world that corresponds to each maintenance worker in the real world. This model is called a maintenance worker agent. The scenario evaluation value calculation unit 103 can create the maintenance worker agent based on the organization and maintenance worker information 1002b in FIG. 6 that is included in the maintenance company information 1002.

[0045] The maintenance personnel agent executes tasks defined in the maintenance task information 1002a of the maintenance company information 1002 shown in FIG. 5. The maintenance personnel agent also reproduces behaviors other than tasks, such as standby, movement, and rest. In this way, the scenario evaluation value calculation unit 103 can calculate various scenario evaluation values ​​for service scenarios by simulating behaviors such as asset operation and failure occurrence, and behaviors of maintenance personnel performing tasks, and recording the time when each event occurred.

[0046] Examples of scenario evaluation values ​​include the operation time, availability rate, production volume, number of failures, number of tasks executed, execution time, maintenance personnel work time, and travel time of each piece of equipment. In particular, the scenario evaluation value calculation unit 103 can calculate the resource tightness of the maintenance company as an example of a scenario evaluation value using the maintenance personnel's work time, travel time, and base standby time. "Resources" refers to human resources (maintenance personnel). An example of an index of resource tightness is the "average availability rate of maintenance personnel," which will be described later. Scenario evaluation values ​​calculated for each service scenario can be subject to quantitative comparison.

[0047] The scenario selection unit 104 judges the quality of each service scenario created by the scenario creation unit 102 based on the scenario evaluation value calculated by the scenario evaluation value calculation unit 103, and selects good service scenario candidates suitable for customer proposals. One example of a criterion for judging the quality of a service scenario is the average availability rate of maintenance personnel. The scenario selection unit 104 sets an acceptable line as a threshold to be applied to the average availability rate of maintenance personnel, and can eliminate service scenarios whose average availability rate of maintenance personnel exceeds the acceptable line.

[0048] Another example of a scenario evaluation value is the equipment availability rate of the equipment covered by the existing contract. If a maintenance company has concluded a contract guaranteeing equipment availability, there is a risk that a penalty will be incurred if the provision of new maintenance services causes the equipment availability rate of the equipment covered by the existing contract to fall below the guaranteed availability rate. The scenario selection unit 104 sets the guaranteed availability rate as a threshold to be applied to the equipment availability rate, and can select a good service scenario that will bring the equipment availability rate of the equipment covered by the existing contract to above the guaranteed availability rate.

[0049] The display unit 105 displays the processing results of the scenario evaluation value calculation unit 103 and the scenario selection unit 104 to the user via the output device 13 such as a display.

[0050] Figure 8 shows an example of the results using the average availability rate of maintenance personnel as a scenario evaluation value. The availability rate of an individual maintenance personnel is defined as the percentage of the maintenance personnel's total working hours that are taken up by maintenance work time and travel time between sites and bases. This value is calculated for each maintenance personnel and the average is the "average availability rate of maintenance personnel" on the vertical axis of Figure 8. The lower the average availability rate of maintenance personnel, the more time they spend on clerical work and waiting at bases, meaning that they have more spare capacity. Therefore, the average availability rate of maintenance personnel can be used as an indicator of resource tightness.

[0051] Another example of a resource pressure index is the "skill holder ratio." The skill holder ratio is defined as the percentage of maintenance personnel who have the necessary skills for a service scenario out of the total number of maintenance personnel. The higher the skill holder ratio, the more stable and relaxed asset maintenance can be carried out.

[0052] Another example of a resource pressure index is "assembly ease." Assemblement ease is defined as the distance between the base where the maintenance personnel belong and the site where the asset to be maintained is located, or the travel time from the base to the site (or the average travel time if multiple maintenance personnel are required). The smaller the assembly ease, the faster asset maintenance can be carried out in an emergency.

[0053] 8, the display unit 105 displays the current situation and three service scenarios A to C as bar graphs for comparison. The error bars accompanying the bar graphs indicate the variation in results when the agent simulation is repeated multiple times under the same conditions, and risk is analyzed by probabilistically evaluating random events such as failures.

[0054] The dashed line in the figure indicates the tolerance line, which is the threshold used by the scenario selection unit 104. Compared to the current situation, in service scenario A, the average availability rate of maintenance personnel has decreased, and the average availability rate of maintenance personnel (resource tightness) has improved. In service scenario B, the average availability rate of maintenance personnel has increased slightly compared to the current situation, and although it is within the tolerance line, there is a risk that it will exceed the tolerance line when the variability in results is taken into account. Therefore, the scenario selection unit 104 judges service scenario B to be "medium risk." In service scenario C, the average availability rate of maintenance personnel has significantly exceeded the tolerance line. Therefore, the scenario selection unit 104 judges service scenario C to be "high risk." The scenario selection unit 104 should exclude service scenario C from the candidate service scenarios to propose to the customer.

[0055] When the ratio of skilled personnel is used in place of the average availability rate of maintenance personnel, the threshold is the statutory daily ratio of skilled personnel that is mandatory for maintenance companies, or the voluntary target ratio of skilled personnel.When the ease of assembly is used in place of the average availability rate of maintenance personnel, the threshold is the statutory or contractual ease of assembly that is mandatory for maintenance companies, or the voluntary target ease of assembly.

[0056] Figure 9 shows an example of the results using the target equipment availability rate of the existing contract as the scenario evaluation value. The target equipment availability rate is the ratio of the time that the equipment covered by the maintenance service under the existing contract is in operation to the total number of hours covered by the existing contract. Generally, it has been found from experience that the higher the average availability rate of maintenance personnel, the lower the quantity and quality of the maintenance service under the existing contract, increasing the chances of equipment failure, and as a result, the target equipment availability rate drops. The example in Figure 9 assumes a situation in which the maintenance company's existing contract guarantees the equipment availability rate for three pieces of equipment, and compares how the equipment availability rates of these pieces of equipment change under service scenarios A to C with the current situation.

[0057] The dashed line in the figure indicates the guaranteed availability rate of the existing contract, which serves as the threshold used by the scenario selection unit 104. In service scenario A, the availability rates of three pieces of equipment have not changed from the current situation, and there is little impact on the existing contract. In service scenario B, the availability rates of two pieces of equipment have increased from the current situation. However, the availability rate of the other piece of equipment is below the guaranteed availability rate, and there is a risk of penalty. Therefore, the scenario selection unit 104 judges service scenario B to be "medium risk." In service scenario C, the availability rates of all pieces of equipment are below the guaranteed availability rate. Therefore, the scenario selection unit 104 judges service scenario C to be "high risk." The scenario selection unit 104 should exclude service scenario C from the candidate service scenarios to propose to the customer.

[0058] The service proposal support device 100 can select service scenario candidates with good scenario evaluation results, taking into consideration the resource situation of the maintenance company and the impact and risks on existing contracts. Furthermore, the service proposal support device 100 can exclude high-risk service scenario candidates whose scenario evaluation values ​​fall outside the allowable value (threshold) before proposing them to the customer.

[0059] The following describes a specific scenario in which the service proposal support device 100 is used. Assume a situation in which a maintenance company has developed a new symptom detection solution and is planning to propose a new service to a customer. One way to use the first embodiment in such a situation is to select customers to whom the proposal will be most effective. In this case, the scenario creation unit 102 creates multiple service scenarios by varying the target facility site as a service parameter, and the scenario evaluation value calculation unit 103 evaluates these scenarios.

[0060] This allows us to quantitatively determine the extent to which the introduction of the predictive maintenance solution to a target equipment site will reduce the maintenance company's resource pressure, and to prioritize customer proposals. Furthermore, by using the target equipment availability rate of an existing contract as the scenario evaluation value, we can quantitatively consider whether the introduction of the predictive maintenance solution will enable us to upgrade a simple maintenance contract to one with an availability guarantee. Note that "provided services" refers to services related to existing contracts. Existing contracts may be implicit contracts or may be explicitly stated in the contract.

[0061] Another use case is risk assessment of insufficient detection performance of a predictive detection solution. A predictive detection solution cannot necessarily detect signs of failure with 100% accuracy. For example, as shown in the service performance section of Figure 7, there is an indicator such as "detecting failures 90 days later with 90% accuracy." In other words, the performance of a predictive detection solution includes detection accuracy and the number of days until a failure can be detected. Introducing a predictive detection solution with poor service performance could lead to an increase in unnecessary responses due to false alarms, which could actually worsen the resource constraints of maintenance companies.

[0062] That is, the scenario creation unit 102 creates multiple service scenarios by varying the service performance (performance of the precursor detection solution) as a service parameter. The scenario evaluation value calculation unit 103 then evaluates these, and the scenario selection unit 104 selects only those service scenarios that improve the scenario evaluation value compared to when no precursor detection solution is used. This makes it possible to know in advance the precursor detection service performance that will produce the desired effect, thereby reducing risk.

[0063] (Processing Procedure) 10 is a flowchart of the processing procedure. As a prerequisite for starting the processing procedure, it is assumed that service target facility information 1001, maintenance company information 1002, and service parameters 1003 are stored in the auxiliary storage device 15 in a completed state.

[0064] In step S201, the service proposal support device 100 receives the service target facility information 1001 (FIG. 4). Specifically, the reception unit 101 receives the service target facility information 1001 from the auxiliary storage device 15.

[0065] In step S202, the service proposal support device 100 receives the maintenance company information 1002 (FIGS. 5 and 6). Specifically, the receiving unit 101 receives the maintenance task information 1002a and the organization and maintenance staff information 1002b from the auxiliary storage device 15.

[0066] In step S203, the service proposal support device 100 receives the service parameters 1003. Specifically, the scenario creation unit 102 receives the service parameters 1003 from the auxiliary storage device 15. As described above, the service parameters received here are the service parameters themselves, such as the service menu and service performance, and multiple service parameter values ​​associated with each of them.

[0067] In step S204, the service proposal support device 100 creates a service scenario. Specifically, first, the scenario creating unit 102 obtains one service parameter value for each service parameter received in step S203. Second, the scenario creating unit 102 creates one service scenario by combining the service parameters acquired in the "first" step of step S204. Third, the scenario creating unit 102 assigns a scenario ID to each of the service scenarios created in the "second" step of step S204. The scenario creating unit 102 repeatedly creates many service scenarios (repeating the first to third steps) so that the service parameter values ​​are exhaustive.

[0068] In step S205, the service proposal support device 100 creates service scenario information 1004 (FIG. 7). Specifically, the scenario creation unit 102 creates service scenario information 1004 including multiple records, with each service scenario to which a scenario ID was assigned in the "third" step S204 is treated as one record. The scenario creation unit 102 accepts a record indicating the "current" contract details input by the user via the input device 12.

[0069] In step S206, the service proposal support device 100 accepts the type of scenario evaluation value. Specifically, the scenario evaluation value calculation unit 103 accepts the type of scenario evaluation value input by the user via the input device 12. For the sake of convenience, it is assumed here that the "average availability rate of maintenance personnel," which is an example of an index of resource tightness, has been input.

[0070] In step S207, the service proposal support device 100 calculates a scenario evaluation value. Specifically, the scenario evaluation value calculation unit 103 executes the agent simulation described above for each record of the service scenario information 1004 created in step S205, and calculates the average availability rate of the maintenance personnel.

[0071] For example, when calculating the average availability rate of maintenance personnel for scenario A in FIG. 7, the scenario evaluation value calculation unit 103 performs the following process. The scenario evaluation value calculation unit 103 identifies the asset models located at “sites a, b, c” stored in the record of scenario A by referring to the service target equipment information 1001 (Figure 4). The scenario evaluation value calculation unit 103 identifies the tasks associated with the identified model and “condition monitoring, predictive maintenance” stored in the record of scenario A by referring to the maintenance task information 1002a.

[0072] The scenario evaluation value calculation unit 103 identifies one or more maintenance personnel who meet the required skills, required number of personnel, and standard work time for the identified task by referring to the organization and maintenance personnel information 1002b. The maintenance personnel identified here are called "specific maintenance personnel." In many cases, multiple specific maintenance personnel are identified. The scenario evaluation value calculation unit 103 calculates the average availability rate of a specific maintenance worker as the ratio of maintenance work time and travel time between sites / bases to the total working time of the specific maintenance worker.

[0073] In addition, in step S206, if the "target equipment operating rate of the existing contract" is input as the scenario evaluation value, the scenario evaluation value calculation unit 103 performs the following process in step S207. The scenario evaluation value calculation unit 103 identifies one or more tasks that a specific maintenance technician is to perform under an existing contract, by referring to the maintenance task information 1002a. The scenario evaluation value calculation unit 103 calculates the availability of one or more assets that are the target of the identified task.

[0074] As described above, the higher the availability rate of a specific maintenance technician, the more tasks the specific maintenance technician will have to handle, and the lower the quality of the maintenance services provided by the specific maintenance technician. This will result in a corresponding decrease in the availability rate of the equipment under existing contracts for which the specific maintenance technician provides maintenance services (the equipment will be more susceptible to breakdowns). The auxiliary storage device 15 may create a model (function) that indicates the relationship (trade-off) between the availability rate of the specific maintenance technician and the availability rate of the equipment for which the specific maintenance technician provides maintenance services, and store the model (function) in the auxiliary storage device 15.

[0075] In step S208, the service proposal support device 100 accepts a threshold. Specifically, the scenario evaluation value calculation unit 103 accepts a threshold to be applied to the scenario evaluation value accepted in step S206, input by the user via the input device 12. The threshold here is, for example, the "tolerance line" in FIG. 8.

[0076] In step S209, the service proposal support device 100 determines the risk. Specifically, the scenario selection unit 104 evaluates the service scenario by applying the threshold (predetermined condition) received in step S208 to the scenario evaluation value. The scenario selection unit 104 classifies the service scenario into one of "high risk," "medium risk," or "no risk" depending on the degree to which the scenario evaluation value deviates from the reference value toward the risk side, and associates the classification result with the service scenario. If the scenario selection unit 104 classifies a service scenario as "no risk," the scenario selection unit 104 has selected that service scenario as the service scenario to be presented to the customer.

[0077] In step S210, the service proposal support device 100 displays the scenario evaluation value and the risk. Specifically, the display unit 105 displays the "resource tightness evaluation result" (FIG. 8) on the output device 13. Note that, if the "target equipment availability rate of the existing contract" is input as the scenario evaluation value in step S206, the display unit 105 displays the "impact evaluation result on the availability rate of the existing contract" (FIG. 9) on the output device 13 in step S210. Thereafter, the processing procedure ends.

[0078] Example 2 Next, a second embodiment will be described with reference to Fig. 11. Fig. 11 is a functional block diagram of a service proposal support device 100 according to the second embodiment. In Fig. 11, the same reference numerals as in Fig. 1 indicate the same components, so a repeated description will be omitted and only the differences will be described.

[0079] In the second embodiment, a result storage unit 106 and a learning unit 107 are added to the first embodiment in Fig. 1. The result storage unit 106 saves the calculation results of the scenario evaluation output by the scenario evaluation value calculation unit 103. The learning unit 107 uses the data in the result storage unit 106 to learn the characteristics of the calculation results for each service scenario. The result storage unit 106 is a database, and the learning unit 107 is a program.

[0080] The scenario creation unit 102 in the first embodiment creates multiple service scenarios by varying the service parameter values ​​of the service parameters 1003 within a predetermined range. However, the number of types of service parameters, the range of change of service parameter values, and the increment of change are often enormous. As a result, evaluating scenarios in which all service parameters are comprehensively changed requires enormous computation time and computational resources, making it impractical. In practice, it is important to limit the number of service scenarios to be evaluated while predicting in advance which service scenarios are expected to produce good results.

[0081] The learning unit 107 receives the calculation results of past scenario evaluations stored in the result storage unit 106 and the service parameters 1003, and learns how the scenario evaluation value changes in response to changes in various service parameters. For example, the learning unit 107 learns the following:

[0082] When the number of "○"s in the service performance category "Detect failures after ○ days with 90% accuracy" changed from "20" to "10", the "average worker availability rate" dropped significantly. When the "○" in the service performance category "Detect failures after ○ days with 90% accuracy" changed from "90" to "20," there was no significant change in the "average worker availability rate."

[0083] In this case, the learning unit 107 sends an instruction to the scenario creation unit 102 to limit the range of service parameter values ​​that the parameter "service performance" can take from "failure after 10 days..." to "failure after 20 days...". By doing so, only service parameters that contribute to improving the scenario evaluation value can be input to the scenario creation unit 102.

[0084] As is clear from the above, it can be said that the learning unit 107 learns the relationship between the calculation results stored in the result storage unit 106 and the values ​​of the service parameters. According to the service proposal support device 100 of the second embodiment, the service scenarios to be evaluated can be limited to candidates that are expected to produce good results, so that a service scenario to be proposed to a customer can be selected quickly.

[0085] (Effects of the Example) (1) The service proposal support device can evaluate a service scenario by evaluating the resource tightness of maintenance personnel of a maintenance company. (2) The service proposal support device can evaluate service scenarios by also evaluating the availability of facilities. (3) The service proposal support device can select a sign detection solution that improves the scenario evaluation value. (4) The service proposal support device can learn to determine service parameter values ​​that make it easier to select a service scenario with a high scenario evaluation value.

[0086] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0087] 1 Maintenance company 11 Central control unit 12 Input Devices 13 Output Devices 14 Main memory 15 Auxiliary storage 16. Communications equipment 21 Network 31 Facilities (Assets) 32 Other Systems 100 Service proposal support device 101 Reception 102 Scenario Creation Department 103 Scenario evaluation value calculation unit 104 Scenario Selection Department 105 Display section 106 Result storage section 107 Learning Department 1001 Information on facilities covered by the service 1002 Maintenance company information 1002a Maintenance task information 1002b Organization / maintenance personnel information 1003 Service Parameters 1004 Service scenario information

Claims

1. a reception unit that receives information on facilities to be serviced and information on maintenance companies including information on maintenance personnel resources; a scenario creation unit that creates a plurality of service scenarios with different service parameter values ​​based on the received information; a scenario evaluation value calculation unit that calculates a scenario evaluation value including at least the resource tightness of the maintenance company for each service scenario; a scenario selection unit that selects a service scenario whose scenario evaluation value satisfies a predetermined condition; a display unit that displays a service scenario that satisfies the predetermined condition; A service proposal support device comprising:

2. The availability of the equipment for which the services are being provided; The scenario evaluation value calculation unit The availability rate of the equipment that is the target of the provided service is used as the scenario evaluation value, The scenario selection unit Using a guaranteed availability rate of the equipment that is the subject of the provided service as the predetermined condition to be applied to the scenario evaluation value; 2. The service proposal support device according to claim 1, wherein:

3. The scenario creation unit creating a service scenario in which the performance of the predictive detection solution is changed as the service parameter; The scenario selection unit selecting only service scenarios that improve the scenario evaluation value compared to a case without the predictive detection solution; The performance of the predictive detection solution is as follows: Including detection accuracy and number of days until detectable failure, 2. The service proposal support device according to claim 1, wherein:

4. a result storage unit for storing the calculation results of the scenario evaluation value calculation unit; a learning unit that learns the relationship between the calculation results stored in the result storage unit and the values ​​of the service parameters; To have 2. The service proposal support device according to claim 1, wherein:

5. The reception unit of the service proposal support device receiving information on the equipment to be serviced and information on the maintenance company including information on maintenance personnel resources; a scenario creation unit of the service proposal support device, Based on the received information, multiple service scenarios with different service parameter values ​​are created, The scenario evaluation value calculation unit of the service proposal support device Calculating a scenario evaluation value including at least the resource tightness of the maintenance company for each service scenario; a scenario selection unit of the service proposal support device, selecting a service scenario whose scenario evaluation value satisfies a predetermined condition; The display unit of the service proposal support device Displaying a service scenario that satisfies the predetermined condition; A service proposal support method characterized by the above.

6. A service proposal support system including a service proposal support device and a facility that is a target of a service provided by the service proposal support device, the service proposal support device, a reception unit that receives information on facilities to be serviced and information on maintenance companies including information on maintenance personnel resources; a scenario creation unit that creates a plurality of service scenarios with different service parameter values ​​based on the received information; a scenario evaluation value calculation unit that calculates a scenario evaluation value including at least the resource tightness of the maintenance company for each service scenario; a scenario selection unit that selects a service scenario whose scenario evaluation value satisfies a predetermined condition; a display unit that displays a service scenario that satisfies the predetermined condition; A service proposal support system comprising:

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