Information processing method and information processing device

Abstract

This information processing method includes: a step (S131) of acquiring a plurality of items of priority degree data representing a priority degree of a recovery work for a stopped facility among a plurality of facilities, for each combination of the facility and the cause for stopping; a step (S133) of estimating, for each item of priority degree data, the facility operation rate of a plurality of designated facilities that a worker performing the recovery work is in charge of among the plurality of facilities; a step of selecting an item of priority degree data for which the operation rate is equal to or more than a threshold value; and a step (S135) of outputting the selected priority degree data.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing method and an information processing device. [Background technology]

[0002] Patent Document 1 discloses a work management support device that aims to facilitate work management. The work management support device in Patent Document 1 sets work priorities and assigns tasks to workers based on the work priorities and worker information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-214905 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the conventional work management support device described above may not be able to set appropriate work priorities, which may not lead to improved productivity.

[0005] Therefore, the present disclosure provides an information processing method and the like that can support improvement in productivity. [Means for solving the problem]

[0006] An information processing method according to one aspect of the present disclosure includes the steps of: acquiring multiple pieces of priority data representing the priority of recovery work for stopped equipment among multiple pieces of equipment, for each combination of the equipment and the cause of the stoppage; estimating, for each piece of priority data, the equipment availability rates of multiple pieces of equipment among the multiple pieces of equipment that are in charge of a worker performing the recovery work; selecting priority data that results in an equipment availability rate equal to or greater than a threshold; and outputting the selected priority data.

[0007] An information processing device according to one aspect of the present disclosure includes an acquisition unit that acquires multiple pieces of priority data that represent the priority of recovery work for stopped equipment among multiple pieces of equipment for each combination of the equipment and the cause of the shutdown; an estimation unit that estimates, for each piece of priority data, the equipment availability rates of multiple pieces of equipment among the multiple pieces of equipment that are the responsibility of a worker performing the recovery work; a selection unit that selects priority data that will result in an equipment availability rate equal to or greater than a threshold; and an output unit that outputs the selected priority data.

[0008] Furthermore, one aspect of the present disclosure can be realized as a program that causes a computer to execute the information processing method, or as a computer-readable non-transitory recording medium storing the program. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to support improvement in productivity. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing the configuration of a factory to which an information processing system according to an embodiment is applied. [Figure 2] FIG. 2 is a block diagram showing a configuration of an information processing system according to an embodiment. [Figure 3] FIG. 3 is a block diagram illustrating a configuration of an information processing device according to an embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the operation performance data. [Figure 5] FIG. 5 is a diagram illustrating an example of work performance data. [Figure 6] FIG. 6 is a diagram illustrating an example of the organization data. [Figure 7] FIG. 7 is a diagram illustrating an example of priority data. [Figure 8] FIG. 8 is a diagram illustrating an example of a method for setting priorities. [Figure 9] FIG. 9 is a diagram illustrating an example of the average work time for each cause of stoppage and the relationship between the priority. [Figure 10] FIG. 10 is a diagram showing an example of the mean time between failures for each piece of equipment and the relationship between the priority. [Figure 11] FIG. 11 is a sequence diagram showing data accumulation processing (learning phase) by the information processing system according to the embodiment. [Figure 12] FIG. 12 is a sequence diagram showing the distribution estimation process (learning phase) performed by the information processing system according to the embodiment. [Figure 13] FIG. 13 is a sequence diagram showing the process (use phase) of determining optimum priority data and optimum organization data by the information processing system according to the embodiment. [Figure 14] FIG. 14 is a flowchart illustrating the process of estimating the task time distribution by the information processing system according to the embodiment. [Figure 15] FIG. 15 is a flowchart illustrating the process of estimating the operating time distribution by the information processing system according to the embodiment. [Figure 16] FIG. 16 is a flowchart showing a schedule creation process performed by the information processing system according to the embodiment. [Figure 17] FIG. 17 is a flowchart illustrating the process of estimating the operating rate of the responsible equipment by the information processing system according to the embodiment. [Figure 18] FIG. 18 is a flowchart illustrating the process of estimating the total downtime and the total operating time performed by the information processing system according to the embodiment. [Figure 19] FIG. 19 is a diagram illustrating an example of virtual facility information. [Figure 20] FIG. 20 shows eight pieces of equipment for which two workers are in charge of restoration work. [Figure 21] FIG. 21 is a diagram showing the mean time between failures and the mean work time for the eight pieces of equipment shown in FIG. [Figure 22A] FIG. 22A is a diagram showing a first composition candidate in the example shown in FIG. [Figure 22B] FIG. 22B is a diagram showing a second composition candidate in the example shown in FIG. [Figure 23] FIG. 23 is a diagram showing the estimation results of the equipment operating rate in the example shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0011] (Summary of the Disclosure) An information processing method according to one aspect of the present disclosure includes the steps of: acquiring multiple pieces of priority data representing the priority of recovery work for stopped equipment among multiple pieces of equipment, for each combination of the equipment and the cause of the stoppage; estimating, for each piece of priority data, the equipment availability rates of multiple pieces of equipment among the multiple pieces of equipment that are in charge of a worker performing the recovery work; selecting priority data that results in an equipment availability rate equal to or greater than a threshold; and outputting the selected priority data.

[0012] This allows the facility availability rate to be estimated for each priority data, making it possible to select appropriate priority data from multiple priority data. Therefore, for example, it becomes possible to notify workers of recovery work based on appropriate priority data, so as to further improve productivity. Therefore, the information processing method according to this aspect can help improve productivity.

[0013] Also, for example, in the estimating step, the ratio of the sum of the operating times of each of the plurality of pieces of equipment to the sum of the operating times of each of the plurality of pieces of equipment and the stoppage times of each of the plurality of pieces of equipment may be estimated as the equipment operating rate.

[0014] This allows the equipment availability rate to be estimated with high accuracy.

[0015] Furthermore, for example, in the selecting step, priority data with the highest equipment availability rate may be selected.

[0016] This allows optimal priority data to be selected, thereby more effectively supporting improvement in productivity.

[0017] Furthermore, for example, an information processing method according to one aspect of the present disclosure may include a step of creating multiple pieces of organization data representing assignment of equipment to each of multiple workers for the multiple pieces of equipment. The estimating step and the selecting step may be performed for each piece of organization data and for each worker. The information processing method may further include a step of estimating, for each piece of organization data, an overall availability rate, which is the overall equipment availability rate of the multiple pieces of equipment, based on the equipment availability rate and priority data of the equipment for which each worker is responsible. The outputting step may output organization data and priority data for each worker that will cause the overall availability rate to be equal to or greater than a threshold.

[0018] This allows for the selection of appropriate organization data, thereby enabling appropriate allocation of responsible equipment to improve productivity. Therefore, the information processing method according to this aspect can support the improvement of productivity.

[0019] Also, for example, an information processing method according to one aspect of the present disclosure may include a step of acquiring organization conditions including the number of the plurality of pieces of equipment and the number of the plurality of workers, and in the creating step, may create a plurality of the organization data based on the organization conditions.

[0020] This allows for the creation of composition candidates that suit the actual circumstances of the production system by obtaining the composition conditions that must be met. By avoiding the creation of composition candidates that clearly do not contribute to improving productivity, the amount of processing required to determine the composition data can be reduced.

[0021] Furthermore, for example, in the outputting step, the organization data with the highest overall availability rate and the priority data of each worker may be output.

[0022] This allows optimum knitting data to be selected, thereby more effectively supporting improvement in productivity.

[0023] Furthermore, for example, an information processing method according to an aspect of the present disclosure may include the steps of: acquiring operation record data representing an operation status of each of the plurality of pieces of equipment and work record data representing a work time of each of the plurality of workers; and estimating an operation time distribution of each of the plurality of pieces of equipment and a work time distribution of each of the plurality of workers based on the operation record data and the work record data. In the step of estimating an equipment availability rate, the equipment availability rate may be estimated for each of the organization data and for each of the workers using the operation time distribution and the work time distribution.

[0024] This makes it possible to improve the accuracy of the estimated equipment availability rate by using the work time distribution and operating time distribution estimated by machine learning, etc. Since priority data and organization data can be selected based on the equipment availability rate with high accuracy, it is possible to more effectively support the improvement of productivity.

[0025] Moreover, an information processing device according to one aspect of the present disclosure includes an acquisition unit that acquires multiple pieces of priority data that represent the priority of recovery work for stopped equipment among multiple pieces of equipment for each combination of equipment and the cause of the stoppage; an estimation unit that estimates, for each piece of priority data, the equipment availability rates of multiple pieces of equipment among the multiple pieces of equipment that are in charge of a worker performing the recovery work; a selection unit that selects priority data that will result in an equipment availability rate equal to or greater than a threshold; and an output unit that outputs the selected priority data.

[0026] This can help improve productivity, similarly to the above-described information processing method.

[0027] Hereinafter, the embodiments will be specifically described with reference to the drawings.

[0028] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection configurations, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components not described in the independent claims are described as optional components.

[0029] Furthermore, each figure is a schematic diagram and is not necessarily an exact illustration. Therefore, for example, the scales of the figures do not necessarily match. Furthermore, in each figure, substantially the same components are given the same reference numerals, and redundant explanations are omitted or simplified.

[0030] (Embodiment) [1. An example of a factory where information processing systems are applied] First, an example of a factory to which an information processing system according to an embodiment is applied will be described with reference to Fig. 1. Fig. 1 is a plan view showing the configuration of a factory 1 to which an information processing system according to the embodiment is applied.

[0031] As shown in FIG. 1, a factory 1 is equipped with a plurality of manufacturing facilities 100. Each of the plurality of manufacturing facilities 100 performs one of a plurality of processes for manufacturing a product. The manufacturing facilities 100 are, for example, component mounting machines, processing devices, assembly devices, etc., but are not limited thereto. The manufacturing facilities 100 produce components by performing the processes and output the produced components.

[0032] The components are, for example, but not limited to, parts included in the final product (i.e., the product) or work-in-progress in the production of the final product. The components are objects used to produce parts or work-in-progress, and do not necessarily have to be included in the final product. Note that the manufacturing equipment 100 may be any equipment related to the production of products, and may also be an inspection device that inspects components, work-in-progress, or products.

[0033] In this specification, "production" not only means creating a final product but also includes processing, assembly, inspection, etc. of components (parts or work-in-progress). For example, the components produced by the manufacturing facility 100 are the components output after the manufacturing facility 100 has performed the assigned process (processing, assembly, inspection, etc.). Furthermore, "manufacturing" is an example of production, and when the final product is an industrial product, "manufacturing" is used in the same sense as "production."

[0034] A plurality of workers 2A to 2E work in a factory 1. In the example shown in Fig. 1, the factory 1 is divided into five blocks A to E, and a worker is assigned to each block. For example, worker 2A is in charge of eight pieces of manufacturing equipment 100 arranged in block A, and performs recovery work when these pieces of manufacturing equipment 100 stop working.

[0035] Of the eight pieces of manufacturing equipment 100 that worker 2A is responsible for, it is possible that two or more pieces of manufacturing equipment 100 will stop operating. The stoppage occurs due to multiple factors (stoppage factors) in each piece of manufacturing equipment 100. Worker 2A performs recovery work for the two or more pieces of manufacturing equipment 100 that are stopped in order according to a predetermined priority. Since an appropriate order in which recovery work is performed leads to improved productivity, it is necessary to determine the priority appropriately.

[0036] Therefore, the information processing system according to this embodiment determines and outputs appropriate priority data for each worker. The priority data is data that indicates the priority for each combination of the manufacturing equipment 100 and the cause of the shutdown. The priority is a numerical value set for each combination of the manufacturing equipment 100 and the cause of the shutdown. Recovery work is performed in descending order of priority (largest numerical value).

[0037] Furthermore, the amount of time required for recovery work for each manufacturing facility 100 generally differs depending on the cause of the shutdown. Furthermore, the mean time between failures also differs for each manufacturing facility 100. Therefore, for example, if manufacturing facilities 100 with short mean time between failures are concentrated and assigned to one worker, the number of manufacturing facilities 100 that are down will increase, resulting in a decline in productivity. Therefore, determining which worker is assigned to perform recovery work for which manufacturing facility among multiple manufacturing facilities 100, i.e., the organization (combination) that indicates the correspondence between the worker and the facility in charge, is important for improving productivity.

[0038] Therefore, the information system according to this embodiment further determines and outputs appropriate organization data. The organization data is data that indicates the allocation of the manufacturing facilities 100 to the respective facilities that the plurality of workers 2A to 2E are responsible for.

[0039] [2. Information Processing System Configuration] A specific configuration of the information processing system according to this embodiment will be described below with reference to FIG.

[0040] FIG. 2 is a block diagram showing the configuration of information processing system 10 according to this embodiment.

[0041] 2, the information processing system 10 includes a plurality of manufacturing facilities 100, an information processing device 200, an input device 300, and a display device 400. The manufacturing facilities 100, the information processing device 200, the input device 300, and the display device 400 are communicably connected to each other via a network 500. The communication between the devices may be wired communication or wireless communication.

[0042] As described above, the multiple manufacturing facilities 100 perform one of multiple processes for manufacturing a product. Each manufacturing facility 100 is provided with one or more sensors and an input unit for detecting the operating status of the manufacturing facility 100 and the operating status of the recovery work. The operating status includes the facility ID of the manufacturing facility 100, the stop time, the cause of the stop, and the operating time. The operating status includes the facility ID of the manufacturing facility 100, the worker ID, the work start time, and the recovery time (operation time). The input unit is, for example, a touch panel or a physical button. The worker can obtain the work start time by inputting the start of the recovery work via the input unit.

[0043] The information processing device 200 selects and outputs appropriate priority data from among a plurality of priority data. Specifically, the information processing device 200 estimates an equipment availability rate for each piece of priority data, and selects and outputs priority data for which the estimated equipment availability rate (responsible equipment availability rate) is equal to or greater than a threshold value. The threshold value is a predetermined value, but is not limited to this. The threshold value may also be an equipment availability rate set for one of the plurality of priority data. In other words, the information processing device 200 may compare the equipment availability rates set for each piece of priority data and select priority data with a higher equipment availability rate than the others. More specifically, the information processing device 200 selects and outputs priority data with the highest equipment availability rate as optimal priority data. The selection of priority data is performed for each worker.

[0044] Furthermore, the information processing device 200 determines and outputs organization data based on the priority data selected for each worker. Specifically, the information processing device 200 estimates an overall operation rate, which is the overall equipment operation rate of the multiple manufacturing facilities 100, and determines and outputs organization data that will make the estimated overall operation rate equal to or greater than a threshold value. The threshold value is a predetermined value, but is not limited to this. The threshold value may also be an overall operation rate estimated for one of the multiple organization data. In other words, the information processing device 200 may compare the estimated overall operation rates for each organization data and select organization data with a higher overall operation rate than the others. More specifically, the information processing device 200 determines and outputs the organization data with the highest overall operation rate as the optimal organization data.

[0045] The information processing device 200 is one or more computer devices equipped with a processor and a memory. The processor reads and executes a program stored in the memory to perform predetermined processing. Note that at least a part of the processing performed by the information processing device 200 may be executed by a dedicated circuit. The specific functional configuration of the information processing device 200 will be described later with reference to FIG. 3.

[0046] The input device 300 is a device that receives a predetermined input to the information processing device 200. The input device 300 is a mouse, a keyboard, a microphone, a touch panel, or the like.

[0047] The input device 300 receives inputs such as instructions to start processing and data required for processing from, for example, a system administrator or user. Specifically, the input device 300 receives inputs of multiple priority candidate data that are candidates for priority data. The input device 300 also receives inputs of organization conditions that are conditions for determining the equipment that each worker is responsible for. Specifically, the organization conditions include the maximum number of pieces of equipment that each worker is responsible for, the number of workers, a list of manufacturing equipment, and simulation conditions.

[0048] The display device 400 is an example of an output device that outputs the processing results of the information processing device 200. The processing results may be selected priority data and / or organization data. The display device 400 is, for example, a liquid crystal display device or an organic EL (Electroluminescence) device. The display device 400 is a touch panel display, and may be integrated with the input device 300.

[0049] Note that the information processing system 10 may include, instead of the display device 400 or in addition to the display device 400, an audio output device that outputs the processing result of the information processing device 200.

[0050] [3. Functional configuration of information processing device] Next, the functional configuration of information processing device 200 according to this embodiment will be described with reference to Fig. 3. Fig. 3 is a block diagram showing the configuration of information processing device 200 according to this embodiment.

[0051] As shown in FIG. 3, the information processing device 200 includes a data acquisition unit 211, a data accumulation unit 212, a distribution estimation unit 213, a model storage unit 214, a priority candidate acquisition unit 221, a condition acquisition unit 222, a formation candidate creation unit 223, an equipment availability estimation unit 224, an optimization determination unit 225, and an output unit 226.

[0052] The data acquisition unit 211 acquires the operating status and the work status from each of the manufacturing facilities 100. The data acquisition unit 211 records the acquired operating status and the work status in the data accumulation unit 212.

[0053] The data accumulation unit 212 creates a database of the operation status and work status and accumulates the data in a storage device as operation performance data and work performance data. The storage device is a non-volatile storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The storage device is provided in the information processing device 200, but may also be provided in another device that can communicate via a network.

[0054] The distribution estimation unit 213 uses the accumulated operation performance data to estimate the operation time distribution, which is the simultaneous distribution of operation time and stoppage causes, for each piece of equipment. Specifically, the distribution estimation unit 213 estimates the operation time distribution by performing machine learning using the operation performance data as input data.

[0055] Furthermore, the distribution estimation unit 213 estimates the work time distribution for each stoppage cause using the accumulated work performance data. Specifically, the distribution estimation unit 213 estimates the work time distribution by performing machine learning using the work performance data as input data.

[0056] The distribution is estimated by constructing a predetermined estimation model through machine learning. The estimation model is, for example, but not limited to, a regression model based on Bayesian estimation. The machine learning method is not particularly limited. For example, a supervised learning method such as a method using a classifier, a method using a support vector machine, a decision tree method, or a deep convolutional neural network method can be used.

[0057] The model storage unit 214 stores the estimation model constructed by machine learning in a storage device. Specifically, the model storage unit 214 stores the work time distribution for each cause of shutdown and the operating time distribution for each piece of equipment. The storage device may be the same as or different from the storage device used by the data accumulation unit 212 to store data.

[0058] The priority candidate acquisition unit 221 acquires a plurality of priority candidate data input via the input device 300 .

[0059] The condition acquisition unit 222 acquires the composition conditions input via the input device 300 .

[0060] The organization candidate creation unit 223 creates a plurality of organization candidate data, which are candidates for organization data. In this embodiment, the organization candidate creation unit 223 creates a plurality of organization candidate data based on the acquired organization conditions.

[0061] The equipment availability estimation unit 224 estimates the equipment availability rates of multiple pieces of equipment that a worker is responsible for (hereinafter referred to as the "responsible equipment availability rate"). Specifically, the equipment availability estimation unit 224 estimates the equipment availability rate of the worker's responsibility based on multiple pieces of priority candidate data and a trained estimation model (work time distribution and operation time distribution). When there are multiple workers, the equipment availability estimation unit 224 estimates the responsible equipment availability rate for each worker.

[0062] In this embodiment, the operating rate of the equipment in charge is the ratio of the total operating time to the sum of the total operating time and the total downtime. The total operating time is the sum of the operating times of each of the multiple pieces of equipment in charge. The total downtime is the sum of the downtime of each of the multiple pieces of equipment in charge. In other words, the operating rate of the equipment in charge is expressed by the following formula (1).

[0063]

number

[0064] The operating time and downtime of each piece of equipment are estimated based on the trained estimation model. A specific method for estimating the operating rate of the equipment will be described later.

[0065] The equipment availability estimation unit 224 also estimates the equipment availability of each worker for each piece of candidate formation data based on the plurality of candidate formation data. Furthermore, the equipment availability estimation unit 224 estimates an overall availability, which is the equipment availability of the entire plurality of manufacturing facilities 100, based on the equipment availability of each worker and the priority data used to calculate the assigned equipment availability for each piece of candidate formation data.

[0066] The overall availability rate is calculated by multiplying the ratio of the number of pieces of equipment that a worker is responsible for to the total number of pieces of equipment by the availability rate of the equipment that the worker is responsible for, and then accumulating the results for each worker.In other words, the overall availability rate is expressed by the following formula (2).

[0067]

number

[0068] The optimization determination unit 225 selects, from among a plurality of priority candidate data, priority data that results in a responsible equipment availability rate equal to or greater than a threshold. Specifically, the optimization determination unit 225 selects priority data with the highest responsible equipment availability rate as the optimal priority data. The selection of priority data is performed for each worker. Furthermore, the selection of priority data for each worker is performed for each composition candidate data.

[0069] Furthermore, the optimization determination unit 225 selects, from among the plurality of composition candidate data, composition data whose overall availability rate is equal to or greater than a threshold value, and priority data selected corresponding to the composition data. Specifically, the optimization determination unit 225 selects the composition data whose overall availability rate is highest as the optimal composition data, and also selects optimal priority data corresponding to the optimal composition data. The optimization determination unit 225 instructs the composition candidate creation unit 223 to create composition data until a sufficient number (for example, a specified number) of candidates are required for selecting the optimal composition data and optimal priority data.

[0070] The output unit 226 outputs the organization data and priority data selected by the optimization determination unit 225. In this embodiment, the output unit 226 outputs the organization data and priority data to the display device 400.

[0071] [4. Information processed by information processing systems] Next, information processed by the information processing system 10 according to the present embodiment will be described with reference to FIGS.

[0072] [4-1. Operational performance data] Fig. 4 is a diagram showing an example of operation performance data. The operation performance data shown in Fig. 4 is data indicating the operation status of each piece of equipment at each time. The operation performance data is compiled into a database by the data accumulation unit 212 based on information acquired by the data acquisition unit 211 and recorded in the storage device.

[0073] As shown in FIG. 4, the operation performance data includes an equipment ID, a time, an operation flag, and a cause of shutdown. The equipment ID is unique identification information assigned to each manufacturing equipment 100. The time is the time when the data acquisition unit 211 acquired the data or the time when the manufacturing equipment 100 transmitted the data. The operation flag is a flag indicating whether the manufacturing equipment 100 is operating or stopped. Here, "1" indicates that the manufacturing equipment 100 is operating, and "0" indicates that the manufacturing equipment 100 is stopped. The cause of shutdown is identification information (stop code) of the cause of the shutdown of the stopped manufacturing equipment 100 (i.e., equipment with an operation flag of "0").

[0074] [4-2. Work performance data] Fig. 5 is a diagram showing an example of work performance data. The work performance data shown in Fig. 5 is data indicating the work time required for recovery work in response to a stoppage that occurs in each piece of equipment. The work performance data is compiled into a database by the data accumulation unit 212 based on information acquired by the data acquisition unit 211 and recorded in the storage device.

[0075] As shown in FIG. 5, the work performance data includes an equipment ID, a work start time, a work duration, and a cause of stoppage. The equipment ID is unique identification information assigned to each manufacturing equipment 100, and in this case represents the stopped manufacturing equipment 100. The work start time is the time when the worker started the recovery work. The work duration is the time required for the worker to perform the recovery work. Note that instead of the work duration, the work end time or the operation time (operation time) of the manufacturing equipment 100 may be included. The cause of stoppage is the cause of the manufacturing equipment 100 being stopped.

[0076] [4-3. Organization Data] Fig. 6 is a diagram showing an example of composition data. The composition data shown in Fig. 6 is data indicating the workers in charge of recovery work for each piece of equipment. The composition data is one selected by the optimization determination unit 225 from multiple composition candidate data created by the composition candidate creation unit 223. The composition data is output to the display device 400 via the output unit 226.

[0077] [4-4. Priority Data] Fig. 7 is a diagram showing an example of priority data. The priority data shown in Fig. 7 is data indicating the priority of recovery work for each combination of equipment and shutdown cause. The priority data is one selected by the optimization determination unit 225 from multiple priority candidate data acquired by the priority candidate acquisition unit 221 via the input device 300.

[0078] [5. Multiple priority candidate data] In this embodiment, the information processing device 200 uses a plurality of priority candidate data. The plurality of priority candidate data are priority data different from each other. Each of the plurality of priority candidate data is created based on a predetermined priority setting method.

[0079] Fig. 8 is a diagram showing an example of a method for setting priority. As shown in Fig. 8, the method for setting priority includes, for example, a method that prioritizes work time, a method that prioritizes equipment performance, and a method based on predetermined rules (rule-based).

[0080] In the method of prioritizing work time, priority is given to the recovery work with the shortest average work time. Specifically, the average work time is calculated for each combination of equipment and shutdown cause, and the shorter the calculated average work time, the higher the priority of the corresponding combination, and the longer the calculated average work time, the lower the priority of the corresponding combination. The combination with the shortest average work time is given the highest priority.

[0081] In the method that prioritizes equipment performance, priority is given to the restoration work of the equipment with the longest mean time between failures. Specifically, the mean time between failures is calculated for each combination of equipment and shutdown cause, and the longer the calculated mean time between failures, the higher the priority of the corresponding combination, and the shorter the calculated mean time between failures, the lower the priority of the corresponding combination. The combination with the longest mean time between failures is given the highest priority. high To burn.

[0082] In the rule-based method, priorities are set based on rules based on on-site experience. By having workers actually perform recovery work in factory 1, they can gain experience in determining which recovery work is most likely to lead to improved productivity in the production system. In the rule-based method, priorities are set based on such on-site experience.

[0083] The method for setting priorities is not limited to the above example. For example, priorities may be set by weighting and combining work time and equipment performance. Alternatively, priorities may be set by correcting, based on a rule base, priorities set based on a method that prioritizes work time or equipment performance.

[0084] By using a plurality of priority candidate data, it becomes possible to select priority data that is appropriate for improving productivity. Examples of undesirable priority data and preferable priority data will be described below.

[0085] Fig. 9 is a diagram showing an example of the average work time for each cause of outage and the relationship with priority. In Fig. 9, the horizontal axis represents the cause of outage, and the vertical axis represents the average work time. The causes of outage are arranged in descending order of average work time.

[0086] In this case, if the method prioritizes work time, a high priority would be set for "factor 008," which has the shortest average work time, and a low priority would be set for "factor 001," which has the longest average work time. However, in the example shown in FIG. 9, the variation in average work time is small, and the effect of setting priorities is low. In other words, no matter which cause is repaired, the average work time remains almost the same, so the effect of shortening the equipment downtime period and extending the operating time is small. In other words, it does not necessarily lead to improved productivity. Therefore, the priority data set for each stoppage cause shown in FIG. 9 based on the method prioritizes work time is not very desirable.

[0087] In contrast, Fig. 10 is a diagram showing an example of the mean time between failures for each piece of equipment and the relationship with priority. In Fig. 10, the horizontal axis represents the equipment ID, and the vertical axis represents the mean time between failures. The equipment IDs are arranged in descending order of mean time between failures.

[0088] In this case, based on the technique that prioritizes equipment performance, a high priority is set for "equipment F001" with the longest mean time between failures, and a low priority is set for "equipment F008" with the shortest mean time between failures. In the example shown in FIG. 10, the mean time between failures varies greatly, so setting priorities is highly effective. In other words, by prioritizing the recovery of equipment with a long mean time between failures, it is expected that the operating time of the entire production system will be extended. Therefore, the priority data set for each piece of equipment shown in FIG. 10 based on the technique that prioritizes equipment performance is preferable priority data for improving productivity.

[0089] As described above, by selecting appropriate priority data depending on the combination of equipment and stoppage causes, it is expected that productivity will be improved.

[0090] [6. Operation] Next, the operation of the information processing system 10 according to this embodiment will be described. The operation of the information processing system 10 according to this embodiment is roughly divided into two processes: a learning phase and a use phase. First, an overview of each process in the learning phase and the use phase will be described using the sequence diagrams of Figs. 11 to 13.

[0091] [6-1. Learning Phase] First, an overview of the learning phase processing will be described with reference to FIGS.

[0092] Fig. 11 is a sequence diagram showing data accumulation processing (learning phase) by the information processing system 10 according to this embodiment. Fig. 12 is a sequence diagram showing distribution estimation processing (learning phase) by the information processing system 10 according to this embodiment. Note that although Fig. 11 shows only one manufacturing facility 100, the same operation is performed in each of the multiple manufacturing facilities 100.

[0093] 4, in this embodiment, one record (recorded data in the row direction) is generated every second for each of the manufacturing facilities 100 and accumulated as operation performance data. That is, the manufacturing facilities 100 periodically (for example, every second) transmit their operation statuses, and the data accumulation unit 212 accumulates the operation performance data as time-series data that represents the status of each facility in a time series.

[0094] 11, when a shutdown occurs in the manufacturing equipment 100 (S1), the manufacturing equipment 100 transmits the equipment ID, the shutdown time, and the cause of the shutdown to the data acquisition unit 211 (information processing device 200). The data acquisition unit 211 transmits the equipment ID, the shutdown time, and the cause of the shutdown transmitted from the manufacturing equipment 100 to the data accumulation unit 212.

[0095] The data storage unit 212 stores the equipment ID, the stop time, and the cause of the stop as operation performance data (S2). Specifically, the operation flag corresponding to the stop time is set to "0" and the cause of the stop is recorded.

[0096] When restoration work is started on the stopped manufacturing equipment 100 (S3), the manufacturing equipment 100 transmits the equipment ID and the work start time to the data acquisition unit 211. The equipment ID and the work start time may be transmitted from a terminal device carried by the worker. The data acquisition unit 211 stores the received equipment ID and work start time (S4).

[0097] When the recovery work is completed and the manufacturing equipment 100 is recovered (starts operation) (S5), the manufacturing equipment 100 transmits the equipment ID and recovery time (operation time) to the data acquisition unit 211. The data acquisition unit 211 transmits the equipment ID, recovery time, and cause of stoppage transmitted from the manufacturing equipment 100 to the data accumulation unit 212.

[0098] The data storage unit 212 stores the equipment ID, the stop time, and the cause of the stop as operation performance data (S6). Specifically, the operation flag corresponding to the stop time is set to "1."

[0099] The data acquisition unit 211 also calculates the work time (S7). Specifically, the data acquisition unit 211 calculates the work time by subtracting the work start time from the recovery time. Then, the data acquisition unit 211 updates the work status (S8). Specifically, the data acquisition unit 211 transmits the calculated work time, equipment ID, work start time, and cause of stoppage to the data accumulation unit 212. The data accumulation unit 212 accumulates the equipment ID, work start time, work time, and cause of stoppage as work performance data (S9).

[0100] The above process is performed for each manufacturing facility 100 for a predetermined period. As a result, the operation performance data and work performance data for the predetermined period are accumulated in the storage device of the data accumulation unit 212. The predetermined period is a relatively long period such as one day, one week, or one month, but is not particularly limited. The larger the amount of data, the higher the accuracy of the distribution estimation by machine learning, which will be described later.

[0101] Next, as shown in Fig. 12, the distribution estimation unit 213 reads out the operation result data and task result data for a predetermined period from the storage device of the data accumulation unit 212, and estimates the operation time distribution using the read out data (S11). The distribution estimation unit 213 also estimates the operation time distribution using the read out data (S12). The specific process of estimating the distribution will be described later. Next, the estimated distribution is stored in the storage device by the model storage unit 214 (S13).

[0102] As described above, according to the information processing system 10 of the present embodiment, in the learning phase, operation performance data and work performance data are collected, and machine learning is performed using the collected data to estimate the operating time distribution and the working time distribution.

[0103] [6-2. Use Phase] Next, an overview of the processing in the use phase will be described with reference to Fig. 13. Fig. 13 is a sequence diagram showing the processing (use phase) of determining optimal priority data and optimal organization data by the information processing system 10 according to this embodiment.

[0104] 13, first, the input device 300 accepts input of priority candidate data and scheduling conditions (S21). The accepted priority candidate data and scheduling conditions are transmitted to the priority candidate acquisition unit 221 and the condition acquisition unit 222 of the information processing device 200, respectively.

[0105] Next, the information processing device 200 performs a composition creation process (S22). In the composition creation process, optimal priority data and optimal composition data are determined. Specific processes will be described later. The determined optimal priority data and optimal composition data are transmitted to the display device 400.

[0106] Next, the display device 400 displays the optimum formation data (S23) and the optimum priority data (S24). Note that the optimum priority data may be displayed before the optimum formation data, or the optimum formation data and the optimum priority data may be displayed simultaneously.

[0107] As described above, according to the information processing system 10 of this embodiment, in the usage phase, the trained model (estimated distribution) obtained by machine learning is used to determine and display optimal organization data and optimal priority data. A manager of the factory 1 or the like can contribute to improving the productivity of the production system by creating an organization of workers and a work sequence for each recovery work based on the displayed optimal organization data and optimal priority data.

[0108] [7. Specific Processing] Next, specific details of the processing performed by the information processing system 10 according to this embodiment will be described with reference to FIGS.

[0109] [7-1. Estimation of work time distribution] First, the specific processing of estimating the work time distribution (step S11 in FIG. 12) will be described with reference to FIG. 14. FIG. 14 is a flowchart showing the processing of estimating the work time distribution by the information processing system 10 according to this embodiment.

[0110] 14, first, the distribution estimation unit 213 of the information processing device 200 reads and acquires work performance data for a predetermined period from the storage device of the data accumulation unit 212 (S101). Next, the distribution estimation unit 213 acquires all stoppage causes included in the work performance data (S102).

[0111] Next, the distribution estimation unit 213 selects one stop cause and narrows down the work performance data corresponding to the selected stop cause (S103). Using the narrowed down work performance data, the distribution estimation unit 213 performs machine learning to estimate the work time distribution of the selected stop cause (S104).

[0112] The distribution estimation unit 213 repeats steps S103 and S104 until the work time distribution is estimated for all stop causes (S105). In the repetition, in step S103, a stop cause for which the work time distribution has not been estimated is selected.

[0113] When the work time distributions for all stoppage causes have been estimated (Yes in S105), the process ends.

[0114] [7-2. Estimation of operating time distribution] Next, the specific process of estimating the operating time distribution (step S12 in FIG. 12) will be described with reference to Fig. 15. Fig. 15 is a flowchart showing the process of estimating the operating time distribution by the information processing system 10 according to this embodiment.

[0115] 15, first, the distribution estimation unit 213 of the information processing device 200 reads and acquires operation performance data for a predetermined period from the storage device of the data accumulation unit 212 (S111). Next, the distribution estimation unit 213 acquires all equipment (specifically, equipment IDs) included in the operation performance data (S112).

[0116] Next, the distribution estimation unit 213 selects one piece of equipment and narrows down the operation history data corresponding to the equipment (S113). Using the narrowed down operation history data, the distribution estimation unit 213 performs machine learning to estimate the operation time distribution (simultaneous distribution of operation time and stoppage causes) of the selected piece of equipment (S114).

[0117] The distribution estimation unit 213 repeats steps S113 and S114 until the operating time distribution is estimated for all the equipment (No in S115). In the repetition, in step S113, equipment for which the operating time distribution has not been estimated is selected.

[0118] When the operating time distributions for all the pieces of equipment have been estimated (Yes in S115), the process ends.

[0119] [7-3. Formation Creation Process] Next, specific processing of composition creation (step S22 in Fig. 13) will be described with reference to Fig. 16. Fig. 16 is a flowchart showing the composition creation processing by the information processing system 10 according to this embodiment.

[0120] 16, the organization candidate creation unit 223 creates organization candidate data (S121). Specifically, the organization candidate creation unit 223 acquires organization candidate data that satisfies the organization conditions acquired by the condition acquisition unit 222.

[0121] Next, the equipment availability estimation unit 224 selects one worker (specifically, the worker ID) (S122), and estimates the optimal priority data of the selected worker and the equipment availability rate for which the worker is responsible (S123). A specific method for estimating the equipment availability rate for which the worker is responsible will be described later with reference to FIG. 17.

[0122] The processes of steps S122 and S123 are repeated until the optimum priority data and the operating rates of the equipment in charge of all workers are estimated (No in S124). In the repetition, in step S122, a worker whose optimum priority data has not been estimated is selected.

[0123] Once the optimal priority data and the associated equipment availability rates for all workers have been estimated (Yes in S124), the equipment availability estimation unit 224 estimates the equipment availability rate (overall availability rate) for the entire process based on the optimal priority data for each worker (S125). The overall availability rate is estimated based on the above-mentioned formula (2). The estimated overall availability rate is stored in the storage device together with the optimal priority data for each worker.

[0124] Next, if the number of times the processes of steps S121 to S125 described above have been executed has not reached the specified number of times (No in S126), the processes of steps S121 to S125 are repeated. Note that, during the repetition, the formation candidate creation unit 223 creates formation candidate data that is different from the already created formation candidate data. By repeating each step, optimal priority data and overall availability for each worker are estimated for each of the multiple formation candidate data.

[0125] If the specified number of times is reached (Yes in S126), the optimization determination unit 225 determines the formation data that maximizes the overall equipment operating rate as the optimal formation data, and the output unit 226 outputs the optimal formation data and the optimal priority data of each worker corresponding to the optimal formation data (S127).

[0126] The number of times specified is a number specified in advance by a system administrator or the like. The more times specified, the greater the number of organization candidate data, and therefore the greater the possibility that more optimal organization data will be created. If the number of times specified is reduced, the processing time will be shorter, making it possible to determine optimal organization data in a short period of time.

[0127] [7-4. Estimation of equipment utilization rate] Next, the process of estimating the operating rate of the equipment in charge of each worker (S123) will be described with reference to Fig. 17. Fig. 17 is a flowchart showing the process of estimating the operating rate of the equipment in charge by the information processing system 10 according to this embodiment.

[0128] 17, first, the equipment availability estimation unit 224 selects one priority candidate data (S131). Next, the equipment availability estimation unit 224 estimates the total downtime and total operation time of each of the multiple pieces of equipment for which the worker is responsible, based on the selected priority candidate data (S132). A specific example of the process of estimating the total downtime and total operation time will be described later with reference to FIG. 18.

[0129] Next, the equipment availability estimation unit 224 estimates the equipment availability for which the worker is responsible based on the estimated total downtime and total operating time (S133). The equipment availability for which the worker is responsible is estimated based on the above-mentioned formula (1). The estimated equipment availability for which the worker is responsible is stored in the storage device in association with the worker.

[0130] The processes of steps S131 to S133 are repeated until the operating rates of the responsible equipment are estimated for all the priority candidate data (No in S134). Estimate Priority candidate data that has not been selected is selected.

[0131] When the responsible equipment operation rates for all priority candidate data have been estimated (Yes in S134), the equipment operation rate estimation unit 224 records the priority data with the highest responsible equipment operation rate and the responsible equipment operation rate in the storage device (S135).

[0132] [7-5. Estimation of total downtime and total operating time] Next, the process of estimating the total downtime and total operation time (S132) will be described with reference to Fig. 18. Fig. 18 is a flowchart showing the process of estimating the total downtime and total operation time by the information processing system 10 according to this embodiment.

[0133] As shown in FIG. 18, first, the equipment availability estimation unit 224 acquires a list of equipment responsible for each worker based on the formation candidate data, and creates virtual equipment information (S141). The virtual equipment information is information used in a simulation for estimating total shutdown time and total operation time, and is, for example, the information shown in FIG. 19. FIG. 19 is an example of virtual equipment information. In the example shown in FIG. 19, each equipment ID is associated with a time until shutdown, a shutdown flag, a shutdown cause, operation time, shutdown time, and total time. For the shutdown flag, "0" indicates that the equipment is in operation, and "1" indicates that the equipment is stopped. The operation time of the equipment is calculated by subtracting the shutdown time from the total time.

[0134] Next, the equipment availability estimation unit 224 records the numerical values ​​of each item of the virtual equipment information based on the trained model (work time distribution and operation time distribution) stored in the model storage unit 214 (S142). Specifically, the equipment availability estimation unit 224 records the time until shutdown, the cause of shutdown, and the operation time sampled from each of the work time distribution and operation time distribution in the virtual equipment information for each piece of equipment. The shutdown time and total time are each recorded as initial values ​​of 0.

[0135] Next, the equipment availability estimation unit 224 decreases the time until the shutdown of all the equipment by a unit time (for example, 1 second) (S143), and then increases the total time of all the equipment by a unit time (for example, 1 second) (S144). Note that the unit time does not have to be 1 second and may be any value set.

[0136] If there is no equipment whose time until shutdown is 0 or less (No in S145), the process returns to step S143 and increases the unit time (for example, 1 second) to be decreased. The unit time decrease of the time until shutdown of all equipment and the unit time increase of the total time of all equipment are repeated until equipment whose time until shutdown is 0 or less is found.

[0137] If there is any equipment whose time until shutdown is 0 or less (Yes in S145), the shutdown flag of that equipment is changed to "1" (S146).

[0138] Next, for each piece of equipment whose shutdown flag is "1", the equipment availability estimation unit 224 calculates a priority in accordance with the priority data (S147). Next, the equipment availability estimation unit 224 selects the equipment with the highest priority among the pieces of equipment whose shutdown flag is "1" (S148), and changes the shutdown flag of the selected piece of equipment to "0" (S149).

[0139] Next, the equipment availability estimation unit 224 updates the information of other equipment by the amount of work time of the selected equipment (S150). Specifically, the equipment availability estimation unit 224 decreases the time until shutdown of the equipment in operation by the amount of work time of the selected equipment, and increases the shutdown time of the equipment that is stopped and the total time of all equipment. Furthermore, for equipment whose time until shutdown is below zero, the equipment availability estimation unit 224 adds the amount by which the time is below zero to the shutdown time.

[0140] Next, the equipment availability estimation unit 224 updates the time until shutdown, shutdown cause, and operation time of the selected equipment to different values ​​sampled from the operation time distribution and operation time distribution (S151). Unless the total time exceeds a predetermined specified time, the processing of steps S145 to S151 described above is repeated (No in S152). If the total time exceeds the specified time (Yes in S152), the processing ends.

[0141] The sum of the stoppage times of each piece of equipment in the virtual equipment information at the time when the total time exceeds the specified time is the total stoppage time. Also, the sum of the values ​​obtained by subtracting the stoppage times from the total time of each piece of equipment is the total operation time.

[0142] As described above, by simulating the shutdown and operation of the equipment, the total shutdown time and total operation time can be estimated. As a result, the equipment operation rate (operation rate of the equipment in charge) is calculated based on the above-mentioned formula (1) (S133 in FIG. 17).

[0143] [Example] The processing of the information processing system 10 according to this embodiment will be described below based on a simple example.

[0144] Fig. 20 is a diagram showing eight pieces of equipment A to H for which two workers X and Y are in charge of recovery work. Fig. 21 is a diagram showing the mean time between failures and average work time for the eight pieces of equipment A to H shown in Fig. 20. Below, an example will be described in which the average work time is the same for workers X and Y, but they may be different.

[0145] Fig. 22A is a diagram showing a first formation candidate for the example shown in Fig. 20. Fig. 22B is a diagram showing a second formation candidate for the example shown in Fig. 20. In the first formation candidate, the equipment for which worker X is responsible is four pieces of equipment A to D, and the equipment for which worker Y is responsible is four pieces of equipment E to H. In the second formation candidate, the equipment for which worker X is responsible is four pieces of equipment A, C, E, and G, and the equipment for which worker Y is responsible is four pieces of equipment B, D, F, and H.

[0146] FIG. 23 is a diagram showing the estimated results of the capacity utilization rate for the example shown in FIG. 20. FIG. 23 shows the capacity utilization rate estimated for each combination of a candidate formation, a priority rule, and a worker. Here, two priority rules are used: a rule that prioritizes work time (work time priority) and a rule that prioritizes equipment performance (equipment performance priority). The capacity utilization rate is obtained as a result of performing the processes shown in FIGS. 17 and 18.

[0147] The overall availability rate is calculated based on the above-mentioned formula (2). In this case, since the number of pieces of equipment that each worker X and Y are responsible for is the same, the overall availability rate is the average value of the availability rate of the equipment that worker X is responsible for and the availability rate of the equipment that worker Y is responsible for.

[0148] 23, the highest overall availability rate is 65%. The highest overall availability rate occurs when the candidate formation is "candidate formation 1," the priority rule for worker X is "equipment performance priority," and the priority rule for worker Y is "work time priority." Therefore, the output unit 226 outputs this information, the assigned equipment availability rate for each worker, and the overall availability rate.

[0149] As shown in Fig. 23, the equipment availability rate for each worker and the overall availability rate may differ depending on the combination of priority rules and composition candidates. By calculating the overall availability rate for each combination, it is possible to select and output an appropriate priority rule (priority data) and composition candidate (composition data).

[0150] Although an example of selecting a combination corresponding to the highest overall availability rate has been shown here, this is not necessarily limited to this. For example, there may be cases where it is sufficient to ensure an overall availability rate equal to or greater than a predetermined value, depending on factors such as the takt time difference between the target equipment and downstream processes. In such cases, the information processing device 200 may select and output a combination with an overall availability rate equal to or greater than a threshold value.

[0151] (Other embodiments) While the information processing method and information processing device according to one or more aspects have been described above based on the embodiments, the present disclosure is not limited to these embodiments. As long as they do not deviate from the gist of the present disclosure, various modifications conceivable by those skilled in the art to the present embodiments and forms constructed by combining components of different embodiments are also included within the scope of the present disclosure.

[0152] For example, in the above embodiment, an example was shown in which multiple workers 2A to 2E are working in the factory 1, but this is not limiting. There may be only one worker working in the factory 1. In this case, the worker is responsible for the recovery work of all of the manufacturing equipment 100 in the factory 1, so the information processing system does not need to create organization data. Furthermore, the information processing system only needs to determine priority data for one worker. In this case, for example, in the flowchart of FIG. 16, only step S123 is executed and the result is output.

[0153] Furthermore, for example, the equipment availability rates of the multiple pieces of equipment that a worker is responsible for are calculated using the total downtime and total operating time of the multiple pieces of equipment that the worker is responsible for, but this is not limited to this. For example, the equipment availability rates of the multiple pieces of equipment that the worker is responsible for may be calculated by calculating the equipment availability rate for each piece of equipment and averaging the calculated equipment availability rates.

[0154] Furthermore, the communication method between the devices described in the above embodiments is not particularly limited. When wireless communication is performed between the devices, the wireless communication method (communication standard) is, for example, short-range wireless communication such as ZigBee (registered trademark), Bluetooth (registered trademark), or wireless LAN (Local Area Network). Alternatively, the wireless communication method (communication standard) may be communication via a wide area communication network such as the Internet. Furthermore, wired communication may be performed between the devices instead of wireless communication. Specifically, wired communication is communication using power line communication (PLC) or a wired LAN.

[0155] Furthermore, in the above-described embodiment, a process executed by a specific processing unit may be executed by another processing unit. Furthermore, the order of multiple processes may be changed, or multiple processes may be executed in parallel. Furthermore, the allocation of components included in the work notification system to multiple devices is one example. For example, components included in one device may be included in another device.

[0156] For example, the processing described in the above embodiments may be realized by centralized processing using a single device (system), or may be realized by distributed processing using multiple devices. Furthermore, the processor that executes the program may be a single processor or multiple processors. That is, centralized processing or distributed processing may be performed.

[0157] In the above embodiments, all or some of the components such as the control unit may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU (Central Processing Unit) or a processor reading and executing a software program recorded on a recording medium such as a hard disk drive or semiconductor memory.

[0158] Furthermore, components such as the control unit may be configured with one or more electronic circuits, each of which may be a general-purpose circuit or a dedicated circuit.

[0159] The one or more electronic circuits may include, for example, a semiconductor device, an integrated circuit (IC), or a large scale integration (LSI). The IC or LSI may be integrated on a single chip or on multiple chips. Although the IC or LSI is referred to here as an IC or LSI, the name may vary depending on the degree of integration, and may be called a system LSI, a very large scale integration (VLSI), or an ultra large scale integration (ULSI). Also, a field programmable gate array (FPGA), which is programmed after the LSI is manufactured, can be used for the same purpose.

[0160] Furthermore, the general or specific aspects of the present disclosure may be realized as a system, an apparatus, a method, an integrated circuit, or a computer program. Alternatively, they may be realized as a computer-readable non-transitory recording medium such as an optical disk, a HDD, or a semiconductor memory on which the computer program is stored. Alternatively, they may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

[0161] Furthermore, various modifications, substitutions, additions, omissions, etc. can be made to the above-described embodiments within the scope of the claims or their equivalents. [Industrial Applicability]

[0162] The present disclosure can be used as an information processing method that can support improvement of productivity, and can be used in, for example, factory management systems and production systems. [Explanation of symbols]

[0163] 1. Factory 2A, 2B, 2C, 2D, 2E workers 10 Information Processing Systems 100 Manufacturing equipment 200 Information processing device 211 Data Acquisition Unit 212 Data Storage Unit 213 Distribution estimation part 214 Model Storage Unit 221 Priority candidate acquisition unit 222 Condition Acquisition Unit 223 Formation Candidate Creation Department 224 Equipment Availability Estimation Unit 225 Optimization Judgment Unit 226 Output section 300 Input Device 400 display device 500 Network

Claims

1. acquiring a plurality of priority data representing the priority of restoration work for a stopped facility among the plurality of facilities for each combination of the facility and the cause of the stoppage; a step of estimating, for each of the priority data, equipment availability rates of a plurality of pieces of equipment that are in charge of a worker performing recovery work, among the plurality of pieces of equipment; selecting priority data that results in an equipment utilization rate equal to or greater than a threshold; outputting the selected priority data; performing the estimating step and the selecting step for each of the workers; Information processing methods.

2. In the estimating step, a ratio of a sum of operation times of each of the plurality of pieces of equipment to a sum of operation times of each of the plurality of pieces of equipment and stop times of each of the plurality of pieces of equipment is estimated as the equipment availability rate. The information processing method according to claim 1 .

3. In the selecting step, priority data with the highest equipment availability is selected. The information processing method according to claim 1 .

4. creating a plurality of pieces of organization data representing assignment of a plurality of workers to the plurality of pieces of equipment, performing the estimating step and the selecting step for each of the organization data and for each of the workers; The information processing method further comprises: a step of estimating an overall availability rate, which is an overall availability rate of the plurality of facilities, for each of the organization data based on the availability rate and priority data of the facilities for which each of the workers is responsible; In the outputting step, organization data and priority data of each worker that make the overall availability rate equal to or greater than a threshold value are output. The information processing method according to any one of claims 1 to 3.

5. The method includes a step of acquiring a composition condition including the number of the plurality of pieces of equipment and the number of the plurality of workers. fruit, In the creating step, a plurality of pieces of organization data are created based on the organization conditions. The information processing method according to claim 4.

6. In the outputting step, the organization data with the highest overall availability rate and the priority data of each worker are output. The information processing method according to claim 4.

7. acquiring operation performance data representing the operation status of each of the plurality of pieces of equipment and work performance data representing the working time of each of the plurality of workers; and estimating an operating time distribution of each of the plurality of pieces of equipment and a working time distribution of each of the plurality of workers based on the operation record data and the work record data, In the step of estimating the equipment availability rate, the equipment availability rate is estimated for each of the organization data and each of the workers using the operating time distribution and the task time distribution. The information processing method according to claim 4.

8. an acquisition unit that acquires a plurality of priority data representing the priority of restoration work for a stopped facility among the plurality of facilities for each combination of the facility and the cause of the stoppage; an estimation unit that estimates, for each of the priority data, equipment availability rates of a plurality of pieces of equipment that are in charge of a worker performing recovery work, among the plurality of pieces of equipment; a selection unit that selects priority data that results in an equipment operating rate equal to or greater than a threshold; an output unit that outputs the selected priority data; the estimation unit estimates the equipment availability rate for each of the workers, the selection unit selects the priority data for each of the workers. Information processing device.

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