Information processing method, estimation model generation method, information processing device, and program

Abstract

An information processing method determines task allocation of production equipment for which a worker is in charge in a production process in which a plurality of workers are in charge of recovery work of the plurality of production equipment. An information processing device acquires stop equipment information indicating a stopped production equipment, acquires stop factor information indicating a stop factor of the stopped equipment, acquires list information classified by a worker indicating production equipment allocated to each worker at a current time point and a stop factor thereof, and determines a worker in charge of the stopped equipment based on the stop equipment information, the stop factor information, and the list information.

Description

Technical Field

[0001] This disclosure relates to information processing methods, methods for generating estimation models, information processing apparatus, and programs. Background Technology

[0002] The work notification method involved in the background art is disclosed in Patent Document 1. This work notification method, in the case of newly stopped production equipment (work target equipment), assigns the operator with the shortest recovery time among multiple operators to be responsible for the work target equipment.

[0003] According to the work notification methods involved in the background technology, the overall productivity of the production process may decrease depending on the situation.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: International Patent Publication No. 2022 / 039080 Summary of the Invention

[0007] The purpose of this disclosure is to provide an information processing method, an estimation model generation method, an information processing device, and a program that can appropriately determine the operator responsible for stopping equipment and thereby improve the overall productivity of the production process.

[0008] In one aspect of this disclosure, the information processing method involves a production process in which one of multiple operators is responsible for the recovery operation of multiple production equipment. The method determines the task allocation of the production equipment to be handled by the operator. An information processing device obtains stop equipment information indicating that the production equipment has stopped (i.e., stopped equipment), obtains stop factor information indicating the stopping factors of the stopped equipment, and obtains a list of production equipment and their stop factors assigned to each operator at the current time, categorized by operator. Based on the stop equipment information, the stop factor information, and the list information, the method determines the operator responsible for the stopped equipment. Attached Figure Description

[0009] Figure 1 This is a simplified diagram illustrating the overall structure of the production system involved in the first embodiment.

[0010] Figure 2 This is a simplified diagram illustrating an example of operational history information.

[0011] Figure 3 This is a flowchart illustrating the processing performed by the processing unit of the learning device according to the first embodiment.

[0012] Figure 4This is a diagram that briefly represents an example of list information.

[0013] Figure 5 This is a flowchart illustrating the processing performed by the processing unit of the decision device according to the first embodiment.

[0014] Figure 6 This is a graph representing the estimated processing of operation time.

[0015] Figure 7 This is a simplified diagram illustrating an example of updated list information.

[0016] Figure 8 This is a flowchart illustrating the processing performed by the processing unit of the learning device according to the second embodiment.

[0017] Figure 9 This is a flowchart illustrating the processing performed by the processing unit of the decision device according to the second embodiment.

[0018] Figure 10 This is a graph representing the estimated values ​​of productivity indicators.

[0019] Figure 11 This is a flowchart illustrating the processing performed by the processing unit of the decision device according to the third embodiment. Detailed Implementation

[0020] (The insights that form the basis of this disclosure)

[0021] In a production process that generates products by operating multiple production machines, multiple workers stand by in the factory to perform recovery operations when production machines stop, with one worker responsible for recovering each machine. Therefore, it is necessary to determine the task allocation for each worker to manage the machines. Traditionally, production line managers have relied on experience and intuition to make these allocations. However, each worker's skill level varies due to factors such as proficiency in their work. Furthermore, even the same worker may have varying levels of expertise depending on the stoppage factor. In other words, the recovery time varies depending on the combination of worker and stoppage factor. Therefore, relying solely on managerial experience and intuition makes it difficult to determine the optimal task allocation that maximizes the overall productivity of the production process.

[0022] The background technology involves a work notification method in the case of newly stopped production equipment (work target equipment), in which the operator with the shortest operation time to restore the work target equipment is assigned to the work target equipment.

[0023] According to the work notification method described in the background art, when an operator is performing restoration work on other production equipment, if they are notified of a task instruction for the target equipment, the restoration work on the other production equipment will be interrupted, or the target equipment will be left in a stopped state until the restoration work on the other production equipment is completed. This situation can still occur even when other operators have available time, and therefore, the overall productivity of the production process may decrease.

[0024] In order to solve the above-mentioned problems, the inventors have obtained the following insights, which led to this disclosure. The insights are to manage by operator a list of production equipment and its stopping factors that are assigned to each operator at the current time. Based on the stopping equipment information, stopping factor information and list information, task allocation is determined. Thus, the operator responsible for stopping the equipment can be appropriately determined, thereby improving the overall productivity of the production process.

[0025] Next, the various methods of this disclosure will be described.

[0026] The information processing method of the first aspect of this disclosure, in a production process in which one of multiple operators is responsible for the recovery operation of multiple production equipment, determines the task allocation of the production equipment to be handled by the operator. The information processing device obtains stop equipment information indicating that the production equipment has stopped, i.e., stopped equipment, obtains stop factor information indicating the stop factors of the stopped equipment, obtains list information classified by operator indicating the production equipment and its stop factors assigned to each operator at the current time, and determines the operator responsible for the stopped equipment based on the stop equipment information, the stop factor information and the list information.

[0027] According to the first method, the list information categorized by operator represents the production equipment assigned to each operator at the current time and its stopping factors. Task allocation is determined based on the stopping equipment information, stopping factor information, and list information, thereby enabling the appropriate determination of the operator responsible for stopping the equipment. As a result, the overall productivity of the production process can be improved.

[0028] The information processing method involved in the second aspect of this disclosure may be: in the first aspect, when determining the operator, the stopping factor information and the list information are input into an operation time model that represents the required time for recovery operations classified by operator and stopping factor, thereby calculating the recovery score when each operator is responsible for the stopped equipment.

[0029] According to the second method, a recovery score is calculated by inputting the stopping factor information and list information into the operation time model. Based on the recovery score, the operator responsible for stopping the equipment can be appropriately determined.

[0030] The information processing method involved in the third aspect of this disclosure may be: in the second aspect, the recovery score represents the elapsed time from the current time point to the predetermined time point when the recovery operation of the stopped equipment is completed, assuming each operator is responsible for the stopped equipment.

[0031] According to the third method, the operator responsible for stopping the equipment can be appropriately determined based on the elapsed time from the current time point to the predetermined time point when the recovery operation of the stopped equipment is completed.

[0032] The information processing method involved in the fourth aspect of this disclosure may be: in the third aspect, when deciding the operator, the operator with the lowest recovery score is assigned to be responsible for the stopping equipment.

[0033] According to method 4, the operator with the shortest elapsed time from the current time to the predetermined time when the equipment recovery operation is completed can be responsible for stopping the equipment. As a result, the overall productivity of the production process can be further improved.

[0034] The information processing method involved in the fifth aspect of this disclosure may be: in any one of the second to fourth aspects, when determining the operator, the frequency of the stopping of the stopping equipment is calculated, and based on at least one of the recovery score and the frequency of the stopping, a productivity index value is calculated for each operator to be responsible for the stopping equipment, and the operator with the best productivity index value is responsible for the stopping equipment.

[0035] According to method 5, by having the operator with the best productivity index value responsible for stopping the equipment, the overall productivity of the production process can be further improved.

[0036] The information processing method involved in the sixth aspect of this disclosure may be: in the fifth aspect, when the stopping equipment includes multiple stopping equipment, when determining the operator, the sum of the productivity index values ​​is calculated for multiple combinations of the multiple operators and the multiple stopping equipment, and one or more operators responsible for the multiple stopping equipment are determined corresponding to the optimal combination of the sum of the productivity index values.

[0037] According to method 6, even when multiple production devices stop simultaneously, it is possible to appropriately determine one or more operators responsible for the multiple stopped devices.

[0038] The information processing method involved in the seventh aspect of this disclosure may be: in any one of the first to sixth aspects, and after determining the operator responsible for stopping the equipment, by updating the list information corresponding to the operator, adding the stopping equipment and its stopping factors to the list information.

[0039] According to method 7, by updating the list information, it is possible to appropriately determine the operator responsible for the newly appearing stopped equipment after the update, based on the updated list information.

[0040] The information processing method involved in the eighth aspect of this disclosure may be: in the seventh aspect, the updated list information is sent to a portable terminal carried by the operator responsible for the stopping equipment.

[0041] According to method 8, by sending the updated list information to a portable terminal, the operator can easily refer to the updated list information and thus be able to properly carry out the restoration work of the stopped equipment under their responsibility.

[0042] The information processing method involved in the ninth aspect of this disclosure may be: in any one of the first to eighth aspects, and, in order to obtain progress information indicating the work progress status of each operator, and if a deviation of more than a predetermined value occurs between the work plan shown in the list information corresponding to a certain operator and the work progress status shown in the progress information about that operator, the task allocation of the multiple production equipment to be handled by the multiple operators is re-determined.

[0043] According to Method 9, if a deviation exceeding a specified value occurs between a worker's work plan and the work progress, the task allocation is re-determined, thereby further improving the overall productivity of the production process.

[0044] The information processing method involved in the 10th aspect of this disclosure may be: in the 9th aspect, when re-determining the task allocation, if the progress of a certain operator's work is faster than the work plan, the certain operator shall be responsible for the unused production equipment assigned to other operators.

[0045] According to method 10, by assigning workers whose work progress is faster than the work plan to be responsible for the unused production equipment assigned to other workers, the overall productivity of the production process can be further improved.

[0046] The information processing method involved in the 11th aspect of this disclosure may be: in the 9th aspect, when re-determining the task allocation, if the progress of a certain operator's work is slower than the work plan, other operators shall be responsible for the unused production equipment assigned to the certain operator.

[0047] According to method 11, by having other operators take charge of the unused production equipment assigned to operators whose work progress is slower than the work plan, the overall productivity of the production process can be further improved.

[0048] The estimation model generation method involved in the 12th aspect of this disclosure is as follows: the estimation model is used to determine the task allocation of production equipment to be handled by a worker in a production process in which one of multiple workers is responsible for the recovery operation of multiple production equipment. The information processing device obtains operation history information related to the multiple production equipment. The operation history information includes: stop equipment information indicating that the production equipment has stopped, stop factor information indicating the stop factors of the stopped equipment, operation start time information indicating the start time of the recovery operation of the stopped equipment, recovery time information indicating the time of recovery of the stopped equipment, and identification information of the worker responsible for the recovery operation of the stopped equipment. The estimation model is generated by machine learning using the operation history information as learning data. The estimation model determines the worker responsible for the stopped equipment based on the stop equipment information, the stop factor information, and a list of production equipment and their stop factors assigned to each worker at the current time point, categorized by worker.

[0049] According to method 12, machine learning can be used to appropriately generate an estimation model for determining the operator responsible for stopping the equipment by using historical operational information as learning data.

[0050] The information processing apparatus according to the 13th aspect of this disclosure, in a production process in which one of multiple operators is responsible for the recovery operation of multiple production equipment, determines the task allocation of the production equipment to be handled by the operator, obtains stop equipment information indicating that the production equipment has stopped, i.e., stopped equipment, obtains stop factor information indicating the stop factors of the stopped equipment, obtains list information classified by operator indicating the production equipment and its stop factors assigned to each operator at the current time, and determines the operator responsible for the stopped equipment based on the stop equipment information, the stop factor information and the list information.

[0051] According to method 13, the list information categorized by operator represents the production equipment and its stopping factors assigned to each operator at the current time. Task allocation is determined based on the stopping equipment information, stopping factor information, and list information, thereby enabling the appropriate determination of the operator responsible for stopping the equipment. As a result, the overall productivity of the production process can be improved.

[0052] The procedure involved in the 14th aspect of this disclosure causes an information processing device to perform processing. In a production process in which one of a plurality of operators is responsible for the resumption of multiple production equipment, the information processing device determines the task allocation of the production equipment to be handled by the operator. The processing obtains stop equipment information indicating that the production equipment has stopped, i.e., stopped equipment, obtains stop factor information indicating the stop factors of the stopped equipment, obtains a list information classified by operator indicating the production equipment and its stop factors assigned to each operator at the current time, and determines the operator responsible for the stopped equipment based on the stop equipment information, the stop factor information and the list information.

[0053] According to method 14, the list information categorized by operator represents the production equipment and its stopping factors assigned to each operator at the current time. Task allocation is determined based on the stopping equipment information, stopping factor information, and list information, thereby enabling the appropriate determination of the operator responsible for stopping the equipment. As a result, the overall productivity of the production process can be improved.

[0054] This disclosure can also be implemented as a program that causes a computer to execute the characteristic structures contained in the method or apparatus described above, or a system that uses the program to perform actions. Furthermore, such a computer program can of course be distributed via computer-readable non-transitory storage media such as CD-ROM (Compact Disc Read Only Memory) or communication networks such as the Internet.

[0055] (Implementation of this disclosure)

[0056] Hereinafter, embodiments of the present disclosure will be described in detail using the accompanying drawings. It is assumed that elements with the same symbol in different drawings represent the same or corresponding elements. Furthermore, the constituent elements, their arrangement, connection methods, and order of operation shown in the following embodiments are examples and are not intended to limit the present disclosure. The present disclosure is limited only by the claims. Therefore, while it may not be necessary to achieve the objectives of this disclosure through the constituent elements of the following embodiments that are not described in the independent claims representing the highest-level concept of the present disclosure, such constituent elements will be described as constituent elements constituting a more preferred embodiment.

[0057] (First Embodiment)

[0058] Figure 1 This is a simplified diagram illustrating the overall structure of the production system according to the first embodiment. The production system is constructed within a factory that produces the products. The production system includes a learning device 1, a decision device 2, a communication network 3, and multiple, i.e., m, production devices 4 (41 to 44). m ) and multiple n workers 5 (51~5n ) Carrying n portable terminals 6 (61~6 n The portable terminal 6 can be a dedicated terminal, or a general-purpose smartphone or tablet terminal, etc.

[0059] Learning device 1 and decision-making device 2 can be computer terminals, edge servers, or on-premises servers within the factory, or cloud servers outside the factory. Learning device 1 and decision-making device 2 can also be composed of a single terminal sharing hardware. Learning device 1 and decision-making device 2 can communicate with production equipment 4 and portable terminal 6 via communication network 3. Furthermore, learning device 1 and decision-making device 2 can communicate with each other via communication network 3. Communication network 3 can be a private network or a public network.

[0060] Within the factory, m production equipment 4 are configured in a prescribed layout. The products produced by production equipment 4 can be final finished products or semi-finished products produced during the manufacturing process. The production performed by production equipment 4 can be manufacturing such as processing or assembly, or product inspection. Each production equipment 4 is assigned an equipment ID as identification information for identifying each production equipment 4.

[0061] Within the factory, n workers 5 are on standby. There are instances where production equipment 4 stops operating due to various factors. When a production equipment 4 stops, the workers 5 on standby in the factory perform the restoration work on that production equipment 4. The restoration work of each production equipment 4 is the responsibility of one of the workers 5. The determining device 2 decides the task allocation for each worker 5 to be responsible for the production equipment 4. That is, in the production process where one of the workers 5 is responsible for the restoration work of multiple production equipment 4s, the determining device 2 decides the task allocation for each worker 5 to be responsible for the production equipment 4. Each worker 5 is assigned a worker ID as identification information.

[0062] Learning device 1 includes a processing unit 11 (information processing device), a storage unit 12, and a communication unit 13. Determination device 2 includes a processing unit 41 (information processing device), a storage unit 42, and a communication unit 43. Processing units 11 and 41 are configured with processors such as CPUs (Central Processing Units). Storage units 12 and 42 are configured with HDDs (Hard Disk Drives), SSDs (Solid State Disks), or semiconductor memory. Communication units 13 and 43 are configured with communication modules that correspond to any communication standard such as IP (Internet Protocol).

[0063] Storage unit 12 stores program 31, operation history information 32, and estimation model 33. Storage unit 42 stores program 61, list information 62, and estimation model 33. Storage units 12 and 42 contain computer-readable non-volatile storage media in which programs 31 and 61 are stored.

[0064] Processing unit 11 has an acquisition unit 21, a learning unit 22, and a control unit 23, which are implemented by the processor of processing unit 11 executing program 31 read from storage unit 12. Processing unit 41 has an acquisition unit 51, a decision unit 52, and a control unit 53, which are implemented by the processor of processing unit 41 executing program 61 read from storage unit 42. Details of the processing content of each part will be described below.

[0065] First, let me explain how the learning phase is handled.

[0066] Figure 2 This is a simplified diagram illustrating an example of the operation history information 32. The operation history information 32 is a database with multiple records. Each record corresponds to a single stop of production equipment 4. Each record has multiple fields related to equipment ID, stop reason, stop time, work start time, recovery time, and operator ID. Equipment ID represents the equipment ID attached to the stopped production equipment 4, i.e., the stopped equipment. Stop reason represents the reason for the stop. Stop reasons include material blockage, product model switching, frequent defects, or maintenance, etc. Stop time represents the moment the stopped equipment stopped. Work start time represents the moment the recovery work of the stopped equipment began. Recovery time represents the moment the recovery work was completed and the stopped equipment resumed operation. Operator ID represents the operator ID attached to the operator 5 who performed the recovery work. The learning device 1 generates the operation history information 32 based on action information or work information received from production equipment 4 or portable terminal 6 via communication network 3. The learning device 1 stores the generated operation history information 32 in storage unit 12. In addition, the operation history information 32 can also be divided into equipment operation performance data containing historical information related to the stopping and recovery of the stopped equipment, and operation performance data containing historical information related to the recovery operation of the operator 5, and managed accordingly.

[0067] Figure 3 This is a flowchart illustrating the processing performed by the processing unit 11 of the learning device 1 according to the first embodiment.

[0068] First, in step S01, the control unit 23 reads the operation history information 32 from the storage unit 12, and the acquisition unit 21 acquires the stop device information, including the device ID of the stop device, based on the operation history information 32.

[0069] Next, in step S02, the acquisition unit 21 acquires stop factor information, including the stop factors of the stopping equipment, based on the operation history information 32.

[0070] Next, in step S03, the acquisition unit 21 acquires operation start time information, including the operation start time of the recovery operation of the stopped equipment, based on the operation history information 32.

[0071] Next, in step S04, the acquisition unit 21 acquires recovery time information, including the recovery time of the stopped equipment, based on the operation history information 32.

[0072] Next, in step S05, the acquisition unit 21 acquires operator information, including the operator ID of the operator 5 responsible for the recovery operation of the stopped equipment, based on the operation history information 32.

[0073] Next, in step S06A, the learning unit 22 uses the stopped equipment information, stopped factor information, operation start time information, recovery time information, and operator information obtained from the operation history information 32 as learning data, and performs machine learning using any algorithm such as a neural network. By using machine learning with multiple learning data sets, the learning unit 22 generates an operation time model 33A representing the required time for recovery operations categorized by operator 5 and stopped factor. The control unit 23 includes the generated operation time model 33A in the estimation model 33 and stores it in the storage unit 12. Furthermore, the control unit 23 sends the estimation model 33 from the communication unit 13 to the decision device 2 via the communication network 3. The decision device 2 stores the received estimation model 33 in the storage unit 42.

[0074] Next, the processing during the utilization phase will be explained.

[0075] Figure 4 This is a simplified diagram illustrating an example of list information 62. List information 62 contains multiple list entries managed by operator 5. Figure 4The table below shows list information 621-623 corresponding to operators 51-53. List information 62 is a database with multiple records. Each record corresponds to a recovery operation. Each record has multiple fields related to priority, device ID, stop factor, and estimated operation time. Priority indicates the order in which the recovery operation is performed by operator 5. Stop devices with a priority of 1 correspond to the stop devices currently being recovered by operator 5. Stop devices with a priority of 2 or lower correspond to the non-operated stop devices that have not yet been recovered by operator 5 at the current time. Device ID indicates the device ID attached to the stop device. Stop factor indicates the reason for the stop device's stop. Estimated operation time indicates the estimated operation time from when operator 5 begins the recovery operation on the stop device until its completion. The estimated operation time varies depending on the combination of operator and stop factor. The estimated operation time contained in the record with a priority of 1 is equivalent to the remaining time of the recovery operation currently being performed by the operator. The decision device 2 updates the list information 62 in real time based on the action information or operation information received from the production equipment 4 or portable terminal 6 via the communication network 3.

[0076] Figure 5 This is a flowchart illustrating the processing performed by the processing unit 41 of the decision device 2 according to the first embodiment.

[0077] First, in step S11, the acquisition unit 51 acquires worker information including worker IDs of all workers 5 waiting in the factory.

[0078] After any production equipment 4 stops and becomes a stopped equipment, in step S12, the acquisition unit 51 acquires stopped equipment information, including the equipment ID of the stopped equipment, based on the action information received from the stopped equipment via the communication network 3.

[0079] Next, in step S13, the acquisition unit obtains stop factor information, including the stop factors of the stopping device, based on the action information.

[0080] Next, in step S14, the control unit 53 reads the list information 62 from the storage unit 42, and the acquisition unit 21 acquires the list information 62.

[0081] Next, in step S15, the control unit 53 reads the estimation model 33 from the storage unit 42, and the decision unit 52 calculates the recovery score in the case where each operator 5 is responsible for stopping the equipment.

[0082] Figure 6This is a diagram representing the estimation process of work time. The decision unit 52 inputs data D1 representing operator information, data D2 representing stopped equipment information, data D3 representing stopped factor information, and data D4 representing list information 62 into the work time model 33A contained in the estimation model 33. From this, it calculates the recovery score when each operator 5 is responsible for stopping the equipment. The recovery score represents the elapsed time from the current time point to the predetermined time point when the recovery work of the stopped equipment is completed, when each operator 5 is responsible for stopping the equipment. The work time model 33A outputs data D10 representing multiple recovery scores associated with multiple operators 5.

[0083] Next, in step S16, the decision unit 52 determines the operator 5 with the lowest recovery score as the operator 5 responsible for stopping the equipment.

[0084] Next, in step S17, after determining the operator 5 responsible for stopping the equipment, the decision unit 52 updates the list information 62 corresponding to the operator 5, adding the stopping equipment and its stopping factors to the list information 62. The control unit 53 stores the updated list information 62 in the storage unit 42.

[0085] Figure 7 This is a simplified diagram illustrating an example of the updated list information 62. Figure 7 In the example shown, the decision unit 52 determines operator 51 as operator 5 responsible for stopping the equipment, and the control unit 53 adds a new record (a record with priority level 2) related to the recovery operation of the stopped equipment in the list information 621 corresponding to operator 51. Furthermore, in Figure 7 In the example shown, the recovery score associated with worker 51 is the total estimated work time, which is 53 (points).

[0086] Next, in step S18, the control unit 53 sends the updated list information 621 to the portable terminal 61 carried by the operator 51 responsible for stopping the equipment. The portable terminal 61 displays the received updated list information 621 on the screen.

[0087] According to this embodiment, the list information 62, categorized by operator 5, shows the production equipment 4 assigned to each operator 5 at the current time and its stopping factors. Task allocation is determined based on the stopping equipment information, stopping factor information, and list information 62, thereby enabling the appropriate determination of the operator 5 responsible for stopping the equipment. As a result, the overall productivity of the production process can be improved.

[0088] Furthermore, according to this embodiment, by inputting the stop factor information and list information 62 into the operation time model 33A to calculate the recovery score, it is possible to appropriately determine the operator 5 responsible for stopping the equipment based on the recovery score.

[0089] Furthermore, according to this embodiment, the operator 5 responsible for stopping the equipment can be appropriately determined based on the elapsed time from the current time point to the predetermined time point when the recovery operation of the stopped equipment is completed.

[0090] Furthermore, according to this embodiment, the operator 5, whose time elapsed from the current point in time to the predetermined point in time when the equipment recovery operation is completed, can be responsible for stopping the equipment. As a result, the overall productivity of the production process can be further improved.

[0091] Furthermore, according to this embodiment, by updating the list information 62, it is possible to appropriately determine the operator 5 responsible for the newly appearing stopped equipment after the update, based on the updated list information 62.

[0092] Furthermore, according to this embodiment, by sending the updated list information 62 to the portable terminal 6, the operator 5 can easily refer to the updated list information 62, and thus can appropriately carry out the restoration work of the stopped equipment under their responsibility.

[0093] Furthermore, according to this embodiment, machine learning can be used to appropriately generate a work time model 33A for determining the operator 5 responsible for stopping the equipment by using the operation history information 32 as learning data.

[0094] (Second Implementation)

[0095] Figure 8 This is a flowchart illustrating the processing performed by the processing unit 11 of the learning device 1 according to the second embodiment.

[0096] First, in step S01, the control unit 23 reads the operation history information 32 from the storage unit 12, and the acquisition unit 21 acquires the stop equipment information based on the operation history information 32.

[0097] Next, in step S02, the acquisition unit 21 acquires the stopping factor information based on the operation history information 32.

[0098] Next, in step S21, the acquisition unit 21 acquires stop time information, including the stop time of the stopping device, based on the operation history information 32.

[0099] Next, in step S03, the acquisition unit 21 acquires the operation start time information based on the operation history information 32.

[0100] Next, in step S04, the acquisition unit 21 acquires the recovery time information based on the operation history information 32.

[0101] Next, in step S05, the acquisition unit 21 acquires operator information based on the operation history information 32.

[0102] Next, in step S06B, the learning unit 22 uses the stop equipment information, stop factor information, stop time information, operation start time information, recovery time information and operator information obtained from the operation history information 32 as learning data to perform machine learning.

[0103] The learning unit 22 generates a job time model 33A by using machine learning with multiple learning datasets, representing the required time for resuming a job categorized by worker (5) and by stopping factors. The learning datasets used to generate the job time model 33A include at least stopping factor information, job start time information, resumption time information, and worker information.

[0104] Furthermore, the learning unit 22 generates a stop occurrence frequency model 33B by using machine learning with multiple learning datasets, representing the frequency of stop occurrences categorized by production equipment 4 and by stop factors. Stop occurrence frequency can also be, for example, the mean time between failures (MTBF). The learning datasets used to generate the stop occurrence frequency model 33B include at least information about the stopping equipment, stop factors, and stop times.

[0105] Furthermore, the learning unit 22 uses the work time model 33A and the stop occurrence frequency model 33B to simulate the stopping of production equipment 4 and the recovery operation performed by operator 5. This generates a productivity indicator value model 33C that calculates productivity indicator values ​​when each operator is responsible for stopping the equipment. The productivity indicator value can be, for example, the production quantity. The productivity indicator value model 33C has settable parameters, which are given appropriate initial parameters at the start of machine learning. The learning unit 22 tries various task assignments through simulation and learns the optimal parameters to achieve the obtained productivity indicator values ​​through machine learning such as reinforcement learning, thereby generating the productivity indicator value model 33C.

[0106] The control unit 23 includes the generated operation time model 33A, stop occurrence frequency model 33B, and productivity index value model 33C in the estimation model 33 and stores them in the storage unit 12. In addition, the control unit 23 transmits the estimation model 33 from the communication unit 13 to the decision device 2 via the communication network 3.

[0107] Figure 9 This is a flowchart illustrating the processing performed by the processing unit 41 of the decision device 2 according to the second embodiment.

[0108] The processing of steps S11 to S15 is the same as in the first embodiment.

[0109] Next, in step S22, the decision unit 52 calculates the frequency of the stopping device stopping.

[0110] Next, in step S23, the decision unit 52 calculates the productivity index values ​​when each operator 5 is responsible for stopping the equipment.

[0111] Figure 10 This is a graph representing the estimation processing of productivity index values. The decision unit 52 inputs data D1 representing operator information, data D2 representing stopped equipment information, data D3 representing stopped factor information, and data D4 representing list information 62 into the work time model 33A contained in the estimation model 33. From this, it calculates the recovery score when each operator 5 is responsible for stopping the equipment. The work time model 33A outputs data D10 representing multiple recovery scores related to multiple operators 5.

[0112] Furthermore, the decision unit 52 inputs data D2 and D3 into the stop occurrence frequency model 33B contained in the estimation model 33, thereby calculating the stop occurrence frequency of the stopping device. The stop occurrence frequency model 33B outputs data D11 representing the stop occurrence frequency of the stopping device.

[0113] Furthermore, the decision unit 52 inputs data D10 and D11 into the productivity index value model 33C contained in the estimation model 33, thereby calculating the productivity index values ​​when each operator 5 is responsible for stopping the equipment. The productivity index value model 33C outputs data D12 representing multiple productivity index values ​​related to multiple operators 5. In addition, when calculating productivity index values, recovery operations for equipment with low stopping frequency are prioritized over equipment with high stopping frequency, thereby improving the overall productivity index values ​​of the production process.

[0114] Next, in step S16, the decision unit 52 determines the operator 5 with the best productivity index value as the operator 5 responsible for stopping the equipment. Furthermore, in cases where multiple production devices 4 stop at the same or similar times, resulting in multiple stopped devices, the decision unit 52 calculates the sum of productivity index values ​​for multiple combinations of multiple operators 5 and multiple stopped devices. The decision unit 52 extracts the combination with the best sum of productivity index values ​​and, corresponding to that combination, determines one or more operators 5 responsible for stopping the multiple stopped devices.

[0115] The processing of steps S17 and S18 is the same as in the first embodiment.

[0116] According to this embodiment, by having operator 5, who has the best productivity index value, take charge of stopping the equipment, the overall productivity of the production process can be further improved.

[0117] Furthermore, according to this embodiment, even when multiple production equipment 4 stops simultaneously, it is possible to appropriately determine one or more operators 5 responsible for the multiple stopped equipment.

[0118] (Third implementation)

[0119] Figure 11 This is a flowchart illustrating the processing performed by the processing unit 41 of the decision device 2 according to the third embodiment.

[0120] First, in step S31, the control unit 53 reads the list information 62 from the storage unit 42, and the acquisition unit 21 acquires the list information 62.

[0121] Next, in step S32, the acquisition unit obtains progress information indicating the work progress status of each operator 5 based on action information or work information received from the production equipment 4 or portable terminal 6 via the communication network 3.

[0122] Next, in step S33, the decision unit 52 determines whether there is a deviation greater than a predetermined value between the work plan shown in the list information 62 corresponding to each operator 5 and the work progress status shown in the progress information of each operator 5. For example, for a stopped device that is being restored by operator 5, the decision unit 52 determines whether the error between the estimated work time and the actual work time is more than 10% of the estimated work time.

[0123] If there is a deviation of more than a predetermined value between the work plan and the work progress (step S33: Yes), then in step S34, the decision unit 52 re-determines the task allocation of the multiple production equipment 4 to be handled by the multiple operators 5. The decision unit 52 re-determines the task allocation using the method of the first embodiment or the second embodiment, taking all the non-operational and stopped equipment related to all operators 5 as the target.

[0124] When reassigning tasks, if a worker 5's work progress is faster than the work plan, or if a worker 5's list information 62 becomes empty, the decision unit 52, after reassignment, will assign the unused stop devices previously assigned to other workers 5 to the aforementioned worker 5. These other workers 5 could, for example, be the worker 5 with the highest recovery score.

[0125] Furthermore, when reassigning tasks, if the progress of a certain worker 5 is slower than the work plan, the decision unit 52 will, after the reassignment, allocate the unused stopped equipment previously assigned to that worker 5 to other workers 5. These other workers 5 could, for example, be the worker 5 with the lowest recovery score.

[0126] Next, in step S35, after re-determining the task allocation, the decision unit 52 updates the list information 62 of the operators 5 related to the re-determination. The control unit 53 stores the updated list information 62 in the storage unit 42.

[0127] Next, in step S36, the control unit 53 sends the updated list information 62 to the portable terminal 6 carried by the operator 5 who is involved in the re-decision. The portable terminal 6 displays the received updated list information 62 on its screen.

[0128] According to this embodiment, when a deviation of more than a predetermined value occurs between the work plan of a certain worker 5 and the work progress, the task allocation is re-determined, thereby further improving the overall productivity of the production process.

[0129] Furthermore, according to this embodiment, by assigning the unused production equipment 4 that was allocated to other workers 5 to the workers 5 whose work progress is faster than the work plan, the overall productivity of the production process can be further improved.

[0130] Furthermore, according to this embodiment, by having other operators take charge of the unused production equipment 4 assigned to operator 5 whose work progress is slower than the work plan, the overall productivity of the production process can be further improved.

[0131] Industrial availability

[0132] This disclosure can be widely applied to production systems where one operator is responsible for the recovery operations of multiple production devices.

Claims

1. An information processing method, in a production process where one of multiple operators is responsible for the restoration of multiple production equipment, determines the task allocation of the production equipment to be handled by the operator, characterized in that, Information processing device Obtain information about stopped production equipment, i.e., stopped equipment. Obtain stop factor information representing the stop factors of the stopping device. Obtain a list of production equipment and its shutdown factors assigned to each operator at the current time, categorized by operator. Based on the stopped equipment information, the stopped factor information, and the list information, the operator responsible for the stopped equipment is determined.

2. The information processing method according to claim 1, characterized in that, When determining the operator, the stopping factor information and the list information are input into the operation time model, which represents the time required for recovery operations categorized by operator and stopping factor. Thus, the recovery score is calculated when each operator is responsible for the stopped equipment.

3. The information processing method according to claim 2, characterized in that, The recovery score represents the elapsed time from the current time to the predetermined time when the recovery operation of the stopped equipment is completed, assuming each operator is responsible for the stopped equipment.

4. The information processing method according to claim 3, characterized in that, When deciding on the operator, the operator with the lowest recovery score is assigned to be responsible for stopping the equipment.

5. The information processing method according to claim 2, characterized in that, When deciding on the operator... Calculate the frequency of the stopping device. Based on at least one of the recovery score and the frequency of stoppages, calculate the productivity index value when each operator is responsible for the stopped equipment. The operator with the best productivity index value shall be responsible for the stopped equipment.

6. The information processing method according to claim 5, characterized in that, In cases where the stopping equipment comprises multiple stopping devices, when determining the operator, For each combination of the multiple operators and the multiple stopped devices, the sum of the productivity index values ​​is calculated. Based on the optimal combination of the sum of the aforementioned productivity index values, one or more operators are selected to be responsible for the multiple stopped devices.

7. The information processing method according to claim 1, characterized in that, and, After determining the operator responsible for stopping the equipment, the stopping equipment and its stopping factors are added to the list information corresponding to the operator by updating the list information.

8. The information processing method according to claim 7, characterized in that, and, The updated list information is sent to the portable terminal carried by the operator responsible for stopping the equipment.

9. The information processing method according to claim 1, characterized in that, and, Obtain progress information indicating the work progress of each worker. If a deviation exceeding a predetermined value occurs between the work plan shown in the list information corresponding to a certain operator and the work progress status shown in the progress information about that operator, the task allocation of the multiple production equipment to be handled by the multiple operators will be re-determined.

10. The information processing method according to claim 9, characterized in that, When reassigning tasks, if a worker's work progress is faster than the work plan, that worker will be responsible for the unused production equipment that was assigned to other workers.

11. The information processing method according to claim 9, characterized in that, When reassigning tasks, if the progress of a certain worker's work is slower than the work plan, other workers shall be responsible for the unused production equipment assigned to that worker.

12. A method for generating an estimation model, said estimation model being used to determine the task allocation of production equipment to be handled by a worker in a production process in which one worker is responsible for the recovery operation of multiple production equipment, characterized in that, Information processing device Obtain operational history information related to the multiple production devices. The operational history information includes: stop equipment information indicating that the production devices have stopped (i.e., stopped devices); stop factor information indicating the reasons for the stoppage of the stopped devices; operation start time information indicating the start time of the recovery operation of the stopped devices; recovery time information indicating the time of recovery of the stopped devices; and identification information of the operator responsible for the recovery operation of the stopped devices. Machine learning, using the operational history information as learning data, generates an estimation model. The estimation model determines the operator responsible for the stopped equipment based on the stopped equipment information, the stopped factor information, and a list of production equipment and its stopped factors assigned to each operator at the current time.

13. An information processing device, in a production process where one of multiple operators is responsible for the recovery operation of multiple production equipment, determines the task allocation of the production equipment to be handled by the operator, characterized in that, Obtain information about stopped production equipment, i.e., stopped equipment. Obtain stop factor information representing the stop factors of the stopping device. Obtain a list of production equipment and its shutdown factors assigned to each operator at the current time, categorized by operator. Based on the stopped equipment information, the stopped factor information, and the list information, the operator responsible for the stopped equipment is determined.

14. A program that instructs an information processing device to perform processing, wherein in a production process in which one of a plurality of operators is responsible for the resumption of work on multiple production equipment, the information processing device determines the task allocation of the production equipment to be handled by the operator, characterized in that, The process, Obtain information about stopped production equipment, i.e., stopped equipment. Obtain stop factor information representing the stop factors of the stopping device. Obtain a list of production equipment and its shutdown factors assigned to each operator at the current time, categorized by operator. Based on the stopped equipment information, the stopped factor information, and the list information, the operator responsible for the stopped equipment is determined.

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