Production management support device, production management support method, and production management support program

Abstract

The objective of the present invention is to achieve both improved productivity and optimal worker health and safety. This production management support device comprises: a worker information acquiring unit that acquires worker information relating to workers engaged in production; an environment information acquiring unit that acquires environment information relating to the environment, including machines used in the production; and an index generating unit that generates, on the basis of the acquired worker information and environment information, a health and safety index relating to work health and safety, and a productivity index relating to the productivity of the work.

Description

Production management support device, production management support method, and production management support program

[0001] The present invention relates to a production management support device, a production management support method, and a production management support program.

[0002] A conventional apparatus for supporting production management has been proposed, which includes a profile information evaluation unit that generates profile information including the work capabilities of workers and the work capabilities of production equipment based on worker information about workers who work in collaboration with production equipment and production equipment information about the production equipment, and further evaluates the degree of change in the profile information based on reference profile information including the worker's reference work capabilities and the production equipment's reference work capabilities, a correction estimation unit that estimates a method of correcting the profile information and the correction effect resulting from the correction method based on the evaluation, and an instruction determination unit that determines correction instructions for workers or production equipment to make a correction based on the corresponding correction method based on the estimated correction effect (see Patent Document 1).

[0003] However, while such devices aim to improve productivity by determining correction instructions for workers or production equipment, they do not take into consideration the safety of workers.

[0004] Therefore, when the determined correction instructions were distributed to workers or production equipment, there was a possibility that the risk of contact between the workers and the production equipment would increase, or that the burden on the workers would increase, resulting in harm to the workers.

[0005] International Publication No. 2022 / 208652

[0006] The present invention has been made in view of the above problems, and aims to achieve both improvement in productivity and optimization of worker safety and health.

[0007] In order to solve the above problems, the present invention provides a production management support device comprising: a worker information acquisition unit that acquires worker information related to workers engaged in production; an environmental information acquisition unit that acquires environmental information related to the environment, including machines used in the production; and an index generation unit that generates a safety and health index related to safety and health and a productivity index related to productivity based on the acquired worker information and environmental information.

[0008] According to the present invention, it is possible to generate a safety and health index related to safety and health and a productivity index related to productivity. This makes it possible to accurately recognize not only productivity but also safety and health using the safety and health index and the productivity index, and by using these indexes, it is possible to achieve both improved productivity and the optimization of worker safety and health. Here, workers engaged in production are not limited to workers who work with or operate machines, including production equipment, or perform work using tools, but also include production managers, machine maintenance personnel, and other people engaged in production.

[0009] Furthermore, in the present invention, the index generation unit may use a learning model that has been machine-learned to learn the worker information, the environmental information, the safety and health index, and the productivity index to generate the safety and health index and the productivity index based on the acquired worker information and environmental information.

[0010] By using a machine-learned learning model, it is possible to generate safety and health indicators and productivity indicators that are appropriate for the purpose based on various worker information and environmental information.

[0011] In addition, in the present invention, the learning model may be a learning model that has been machine-learned to learn worker information related to the worker and environmental information related to the environment within a certain time range and a certain area.

[0012] In this way, it is possible to generate productivity indicators and safety and health indicators that are interrelated with each other regarding work, thereby obtaining useful information for achieving both improved productivity and optimized safety and health. Here, the certain time range includes, for example, but is not limited to, the production period of a certain product, a period preset by a user, etc. Furthermore, the certain region includes, but is not limited to, the production area of ​​a certain product, each floor or each line of a factory, etc.

[0013] In the present invention, the worker information may include biometric information of the worker.

[0014] By including biometric information on workers in worker information, it becomes possible to achieve both improved productivity and optimized safety and health, taking into account the physical and mental state of workers.

[0015] In the present invention, the worker information may include worker profile information of the worker.

[0016] The worker profile information is information specific to each worker. By including the worker profile information in the worker information, it becomes possible to simultaneously improve productivity and optimize safety and health by taking into account the information specific to each worker.

[0017] In the present invention, the environmental information may include machine profile information of the machine.

[0018] The machine profile information is information specific to a machine. By including the machine profile information in the environmental information, it is possible to achieve both productivity improvement and optimization of safety and health, taking into account the machine-specific information.

[0019] In addition, in the present invention, the worker information acquisition unit may acquire time information associated with the worker information and the environmental information, and the environmental information acquisition unit may acquire location information associated with the worker and the environmental information.

[0020] In this way, it is possible to generate productivity indicators and safety and health indicators that are interrelated with each other regarding work using time information and location information, thereby obtaining useful information for achieving both improved productivity and optimized safety and health.

[0021] The present invention may further comprise a screen generation unit that generates a screen displaying time-varying changes of the safety and health index and the productivity index.

[0022] This allows us to recognize the changes over time in both safety and health indicators and productivity indicators, and by using these indicators, we can achieve both improved productivity and optimized worker safety and health.

[0023] In the present invention, the screen generation unit may generate a screen that displays the safety and health index and the productivity index in association with a time axis.

[0024] Since it is possible to compare and recognize the temporal relationship between changes in safety and health indicators and productivity indicators, these indicators can be used to achieve both improved productivity and optimized worker safety and health.

[0025] The present invention may further comprise a countermeasure decision unit that decides countermeasures for ensuring the safety of the worker based on the safety and health index and the productivity index generated by the index generation unit.

[0026] This makes it possible to provide a countermeasure that can achieve both improved productivity and optimized safety and health.

[0027] In addition, the present invention may further include a safety and health target receiving unit that receives input of targets for the safety and health indexes, and a productivity target receiving unit that receives input of targets for the productivity indexes, and the countermeasure decision unit may decide on the countermeasures based on the received targets for the safety and health indexes and targets for the productivity indexes.

[0028] This provides a solution that can achieve desired productivity and health and safety.

[0029] In addition, the present invention may further include a production plan acquisition unit that acquires a production plan including the workers and the machines, and the countermeasure may include changing the production plan.

[0030] In this way, it is possible to improve productivity and optimize safety and health by taking production plans into consideration.

[0031] In addition, in the present invention, the worker information may include worker profile information of the worker, and the countermeasure may include updating or adding the worker profile information.

[0032] This allows future production plans to be drawn up based on the new worker profile information, making it possible to achieve both improved productivity and optimized safety and health.

[0033] In the present invention, the countermeasure may include at least one of information for the worker and information for the machine.

[0034] The countermeasures include at least one of information about the worker and information about the machine, so that the countermeasures can be reflected in at least one of the worker and the machine. Information about workers includes, but is not limited to, updates to work procedures and manuals, and work instructions for workers. Information about machines includes, but is not limited to, control commands for machines, installation of guards, changes to the placement and location of machines, and the like.

[0035] In the present invention, a worker information detection unit that detects the worker information may be provided.

[0036] In the present invention, an environmental information detection unit that detects the environmental information may be provided.

[0037] The present invention is a production management support method including the steps of: acquiring worker information about workers engaged in production; acquiring environmental information about the environment, including machines used in the production; generating a safety and health index related to safety and health based on at least the worker information; and generating a productivity index related to productivity based on at least the environmental information.

[0038] According to the present invention, it is possible to generate a safety and health index related to safety and health and a productivity index related to productivity. As a result, not only productivity but also safety and health can be accurately recognized using the safety and health index and the productivity index, and these indexes can be used to achieve both improvement in productivity and optimization of worker safety and health.

[0039] The present invention may also include a step of determining a countermeasure for ensuring the safety of the worker based on the safety and health index and the productivity index.

[0040] This makes it possible to provide a countermeasure that can achieve both improved productivity and optimized safety and health.

[0041] The present invention is a production management support program that causes a computer to execute the production management support method.

[0042] According to the present invention, it is possible to improve productivity while optimizing worker safety and health.

[0043] FIG. 1 is a diagram illustrating the overall configuration of a production management system according to an embodiment of the present invention. FIG. 2 is a diagram illustrating the hardware configuration of a production management support device according to an embodiment of the present invention. FIG. 3 is a diagram illustrating the hardware configuration of a control device according to an embodiment of the present invention. FIG. 4 is a functional block diagram of the production management support device according to an embodiment of the present invention. FIG. 5 is a flowchart illustrating a processing procedure for man-hour measurement according to an embodiment of the present invention. FIG. 6 is a diagram illustrating an example of basic data for calculating human productivity according to an embodiment of the present invention. FIG. 7 is a diagram illustrating an example of basic data for calculating overall equipment effectiveness according to an embodiment of the present invention. FIG. 8 is a diagram illustrating an example of a safety and health index according to an embodiment of the present invention. FIG. 9 is a diagram illustrating another example of a safety and health index according to an embodiment of the present invention. FIG. 10 is a diagram illustrating an example of a display of a productivity index and a safety and health index according to an embodiment of the present invention. FIG. 11 is a diagram illustrating a learning process of an index generation model according to an embodiment of the present invention. FIG. 12 is a flowchart illustrating the procedure for determining a countermeasure according to an embodiment of the present invention. FIG. 13 is a flowchart illustrating another example of the procedure for determining a countermeasure according to an embodiment of the present invention. FIG. 14 is a functional block diagram of a simulation unit according to an embodiment of the present invention.

[0044] [Application Example] Application examples of the present invention will be described below with reference to the drawings. Fig. 1 shows a schematic configuration diagram of a production management system 100 including a production management support device 120 to which the present invention is applied.

[0045] The production management system 100 includes a production management device 110, a production management support device 120, a control device 130, a robot 140, an image sensor 150, a work environment sensor 160, a display device 170, a biometric information sensor 180, an alarm device 190, and a safety device 200.

[0046] The production management device 110 controls and monitors the production line including the robot 140 and the worker P, and acquires and records the production results.

[0047] The production management support device 120 supports production management of a production line including a robot 140 and a worker P.

[0048] The control device 130 controls the robot 140 , the image sensor 150 , the working environment sensor 160 , the display device 170 , the alarm device 190 and the safety device 200 .

[0049] The robot 140 is an example of a machine of the present invention. The image sensor 150 photographs the robot 140 and the worker P. The work environment sensor 160 is a thermometer or the like that detects work environment information such as temperature in a spatial range that includes at least the location where the worker P works. The biological information sensor 180 is a device such as an electrocardiograph that detects biological information (vital information) of the worker P, such as heart rate and heart rate variability. The alarm device 190 is a device such as a patrol lamp that issues an alarm to the worker P. The safety device 200 is a device such as a proximity sensor or photoelectric sensor that serves as a safety sensor used for the safety of the worker.

[0050] 4 is a block diagram showing the functional configuration of the production management support device 120. It includes a worker profile information acquisition unit 11, a worker profile information storage unit 1212, a worker motion information acquisition unit 13, a worker motion information storage unit 14, a worker biometric information acquisition unit 15, a worker biometric information storage unit 16, a machine profile information acquisition unit 17, a machine profile information storage unit 18, a machine motion information acquisition unit 19, a machine motion information storage unit 20, a work environment information acquisition unit 21, a work environment information storage unit 22, an image analysis unit 23, an index generation unit 24, a production status evaluation unit 25, an index storage unit 26, an evaluation criterion information storage unit 27, an evaluation result storage unit 28, a simulation unit 29, a countermeasure determination unit 30, an improvement information generation unit 31, an input unit 32, and an output unit 33.

[0051] The worker profile information acquired by the worker profile information acquisition unit 11 is information specific to the worker, and includes personal attribute information such as name, labor attribute information such as ID, qualification information such as licenses, and work ability information such as work speed.

[0052] The worker movement information is information that indicates the position, posture, and physical movements of the worker.

[0053] Worker biometric information includes heart rate, heart rate variability, acceleration (impact and vibration on the body), respiratory rate, and skin electrical activity due to sweating.

[0054] Machine profile information is information specific to a machine, and includes specification information such as model name, operational information such as line name, performance information such as cumulative operating time, machine capability information such as operating speed, and risk assessment information.

[0055] The machine operation information includes the operating status of the machine, the values ​​of parameters related to operation, whether or not a malfunction has occurred, and measurement data such as vibrations measured by sensors installed inside the machine.

[0056] Work environment information is information about the worker's work environment, and includes temperature, humidity, illuminance, carbon monoxide concentration, amount of suspended dust, noise level, vibration level, risk assessment information, etc. Work environment information also includes the presence or absence of obstacles and other objects in the work environment, information about the location where the worker is engaged in production, such as production line A, and information about the type of work that will be performed in that location.

[0057] This worker profile information, worker movement information, worker biometric information, machine profile information, machine movement information and work environment information are each associated with time information so as to be synchronized within a certain period (time range), and information related to location information can be extracted as information within a certain area.

[0058] The index generation unit 24 of the production management support device 120 generates evaluation indexes for evaluating the production status. Here, a productivity index that evaluates the production status from the perspective of productivity and a safety and health index that evaluates the production status from the perspective of safety and health are generated based on worker profile information, worker operation information, worker biometric information, machine profile information, machine operation information, and work environment information.

[0059] Figure 10 is a graph showing productivity indicators, such as performance utilization rate and work efficiency, and safety and health indicators, such as near-miss incidents, on the same time axis. In the production management support device 120, this information can be displayed on the display that constitutes the output unit 33. By comparing and contrasting productivity indicators and safety and health indicators on the same time axis, it is possible to clearly identify, from the area enclosed by the dashed dotted line, the occurrence of near-miss incidents by workers due to brief machine stoppages. Therefore, rather than simply examining the production situation from the perspective of productivity, it is possible to consider worker safety and health as well, thereby achieving a balance between improving productivity and optimizing safety and health for improvement.

[0060] [Embodiment 1] Hereinafter, with reference to the drawings (including the drawings already described in the application examples), a detailed description of the embodiments of the present invention will be given. However, unless otherwise specified, the specific configurations described in the embodiments are not intended to limit the scope of the present invention to those specific configurations.

[0061] (Production Management System) FIG. 1 is a schematic diagram of a production management system 100 including a production management support device 120 according to this embodiment. The production management system 100 includes a production management device 110, a production management support device 120, a control device 130, a robot 140, an image sensor 150, a work environment sensor 160, a display device 170, a biometric information sensor 180, an alarm device 190, and a safety device 200. The control device 130, the robot 140, the image sensor 150, the work environment sensor 160, the display device 170, the alarm device 190, and the safety device 200 are communicatively connected via a field network NT1. The control device 130 is communicatively connected to the production management device 110 and the production management support device 120 via a higher-level network NT2. Here, the biometric information sensor 180 is connected to the higher-level network NT2. The network configuration is not limited to this. The number of robots 140 included in this production management system 100 is not limited; for the sake of explanation, only one robot 140 is shown in FIG. 1 . Furthermore, the robot 140 is an example of a machine to which the production management system 100 is applied, and other machines also include general manufacturing equipment, conveying equipment, inspection equipment, etc. Although only one worker P is shown in FIG. 1, the number of workers P that are targets of the production management system 100 is not limited. In the following, an example of a worker P will be described as a worker who works together with a machine, but the worker P is not limited to this and may be any person who is engaged in work directly or indirectly related to a machine.

[0062] The production management device 110 controls and monitors the production line including the robots 140 and workers P, and acquires and records production results. The production management device 110 also has a production plan database (DB) 111. The production management device 110 is configured with computers, workstations, etc., and the hardware configuration of the production management device 110 is similar to that of the production management support device 120 described below, so a description thereof will be omitted. The production management device 110 does not necessarily have to be realized by a single physical configuration, and may be configured by multiple computers that work together.

[0063] The production management support device 120 supports production management of a production line including a robot 140 and a worker P. As shown in FIG. 2, the production management support device 120 is configured by a computer, workstation, or the like. Such a computer or the like includes a processor 121 such as a CPU or DSP, a main memory unit 122 such as a read-only memory (ROM) or random access memory (RAM), an auxiliary memory unit 123 such as an EPROM, a hard disk drive (HDD), or removable media, a communication unit 124 for communicating with devices such as the production management device 110 and the control device 130, an input unit 125 such as a keyboard or mouse, and an output unit 126 such as a display, all of which are connected to each other by a bus B1. The removable media may be, for example, a flash memory such as a USB memory or an SD card, or a disc recording medium such as a CD-ROM, DVD, or Blu-ray disc. The auxiliary memory unit 123 stores an operating system (OS), various programs such as an evaluation index generation processing program and a countermeasure proposal determination processing program, various tables, etc. The programs stored therein are loaded into the work area of ​​the main memory unit 122 and executed. The execution of the programs controls each component, thereby realizing each functional unit that serves a specific purpose, as described below. However, some or all of the functional units may be realized by hardware circuits such as ASICs or FPGAs. However, the production management support device 120 does not necessarily have to be realized by a single physical configuration, and may be composed of multiple computers that work together. Here, the input unit 125 corresponds to a safety and health target receiving unit and a productivity target receiving unit that receive input of safety and health indicators and / or productivity indicator targets, as described below.

[0064] The control device 130 controls the robot 140 by outputting control commands to the robot 140 to specify the driving, stopping, and operating speed of the robot 140. The control device 130 also controls the image sensor 150 and the working environment sensor 160, acquires data output from these sensors, and transmits the data to the production management device 110 and the production management support device 120 as needed. The control device 130 also controls the display device 170, the alarm device 190, and the safety device 200. As shown in FIG. 3 , the control device 130 includes a processor 131 such as a CPU or a DSP, a main memory unit 132 such as a read-only memory (ROM) or a random access memory (RAM), an auxiliary memory unit 133 such as an EPROM, a hard disk drive (HDD), or removable media, a communication unit 134 for communicating with devices such as the production management device 110, the production management support device 120, and the image sensor 150, an input unit 135 such as a keyboard or a mouse, and an output unit 136 such as a display, all of which are connected to one another by a bus B2. The removable media may be, for example, a flash memory such as a USB memory or an SD card, or a disk storage medium such as a CD-ROM, DVD, or Blu-ray disc. The auxiliary memory unit 133 stores an operating system (OS), a control program 133a created for each controlled object, such as a machine (including the robot 140), a safety program 133b executed to ensure the safety of the worker P during collaborative work between the robot 140, and a robot control table 133c referenced during execution of the safety program 133b. The programs stored therein are loaded into the work area of ​​the main memory unit 132 and executed. By controlling each component through program execution, various functional units that perform specific purposes, as described below, can be realized. However, some or all of the functional units may be realized using hardware circuits such as ASICs or FPGAs. However, the control device 130 does not necessarily have to be realized using a single physical configuration; it may be composed of multiple computers working together.The control device 130 may be a programmable logic controller (PLC) that executes a control program 133a (which may include sequence control and motion control), but the control device 130 is not limited to this as long as it is a device that controls the robot 140.

[0065] The robot 140 is driven based on a control command from the control device 130, operates at a specified speed, and stops as necessary.

[0066] The image sensor 150 captures images of the robot 140 and the worker P. The image sensor 150 may be a normal camera (2D camera) or a stereo camera (3D camera). Alternatively, a sensing device capable of acquiring a distance profile to an object, such as a laser scanner or a distance measuring sensor, may be used. The image sensor 150 corresponds to the worker information detection unit and the environmental information detection unit of the present invention.

[0067] The work environment sensor 160 detects work environment information relating to a spatial range including at least the location where the worker P performs work. The work environment information includes, but is not limited to, temperature, humidity, illuminance, carbon monoxide concentration, amount of suspended dust, noise level, vibration level, etc. The work environment sensor 160 includes, but is not limited to, a thermometer, a hygrometer, a carbon monoxide sensor, a dust meter, a sound level meter, a vibration meter, etc. The work environment information also includes information such as the presence or absence of objects (including structures) such as obstacles in the work environment of the worker P. The image sensor 150 described above also functions as a work environment sensor that detects the presence or absence of such obstacles. The work environment sensor 160 corresponds to the environment information detection unit of the present invention.

[0068] The display device 170 is a device that displays information to be provided to the worker P.

[0069] The biological information sensor 180 is a device that detects biological information (vital information) of the worker P. Examples of the biological information sensor 180 include, but are not limited to, an electrocardiogram sensor, a pulse wave sensor, an acceleration sensor, and an electrodermal activity sensor. Examples of biological information detected by the biological information sensor 180 include, but are not limited to, heart rate, heart rate variability, acceleration (impact and vibration on the body), respiratory rate, and electrodermal activity due to sweating. The biological information sensor 180 corresponds to the work information detection unit of the present invention.

[0070] The alarm device 190 is a device that issues an alarm to the worker P, and includes a patrol lamp, a speaker, a siren, etc. The display device 170 described above can also be used as the alarm device. Also, a mobile terminal carried by the worker P can also be used as the alarm device.

[0071] The safety device 200 is a device used for worker safety, and includes a proximity sensor or photoelectric sensor as a safety sensor, a safety fence, a light curtain, an interlock switch, or the like.

[0072] (Production Management Support Device) Figure 4 is a block diagram showing the functional configuration of the production management support device 120. It includes a worker profile information acquisition unit 11, a worker profile information storage unit 1212, a worker motion information acquisition unit 13, a worker motion information storage unit 14, a worker biometric information acquisition unit 15, a worker biometric information storage unit 16, a machine profile information acquisition unit 17, a machine profile information storage unit 18, a machine motion information acquisition unit 19, a machine motion information storage unit 20, a work environment information acquisition unit 21, a work environment information storage unit 22, an image analysis unit 23, an index generation unit 24, a production status evaluation unit 25, an index storage unit 26, an evaluation criterion information storage unit 27, an evaluation result storage unit 28, a simulation unit 29, a countermeasure determination unit 30, and an improvement information generation unit 31. The production management support device 120 also includes a screen generation unit that generates screen information to be displayed on a display constituting the output unit 126 or to be displayed on an external device such as a display device 170 via the communication unit 124. Each of these functional units is realized by loading a program stored in the auxiliary memory unit 123 into the main memory unit 122 and having the processor 121 interpret and execute the program. The worker profile information acquisition unit 11, the worker motion information acquisition unit 13, and the worker biometric information acquisition unit 15 constitute the worker information acquisition unit of the present invention. Furthermore, the machine profile information acquisition unit 17, the machine motion information acquisition unit 19, and the work environment information acquisition unit 21 constitute the environment information acquisition unit of the present invention.

[0073] The worker profile information acquisition unit 11 acquires worker profile information, which is information specific to each worker. The worker profile information includes personal attribute information specific to each worker, such as name, gender, age, health check results (height, weight, presence and content of chronic illnesses), presence and content of work restrictions, and labor attribute information, such as ID, department, job rank, qualifications, years of experience, work experience, working hours, overtime hours, and vacation history. The worker profile information also includes work capacity information, such as work speed for individual tasks and the rate of work defects. The labor attribute information may also include interpersonal information, which is information about people related to the worker during work (such as workers working on the same line or managers). The worker profile information may also include a production plan related to the worker. The worker profile information acquisition unit 11 can acquire personal attribute information from a personnel management database, labor attribute information from a labor management database, and work capacity information from the production management device 110, each via a network. Here, the worker profile information acquisition unit 11 corresponds to the production plan acquisition unit of the present invention.

[0074] The worker profile information storage unit 12 is a specified area of ​​the auxiliary storage unit 123 that stores the worker profile information acquired by the worker profile information acquisition unit 11.

[0075] The worker motion information acquisition unit 13 acquires worker motion information related to worker motion. The worker motion information is information indicating the position, posture, and physical movements of the worker. The worker motion information acquisition unit 13 can analyze images captured by the image sensor 150 using the image analysis unit 23 described below and acquire extracted worker motions in real time. The worker motion information acquisition unit 13 can also acquire worker motion information from detection devices worn or carried by the worker, such as an acceleration sensor.

[0076] The worker movement information storage unit 14 is a predetermined area of ​​the auxiliary storage unit 123 that stores the worker movement information acquired by the worker movement information acquisition unit 13. The worker movement information is stored in association with the target worker P using the ID of the worker P or the like. The worker movement information storage unit 14 stores the worker movement information in association with the time the image was captured. The worker movement information may also be stored in association with the installation location of the image sensor that captured the image or a position or spatial range identified by the analysis results.

[0077] The worker biometric information acquisition unit 15 acquires biometric information of the worker. As described above, the biometric information includes heart rate, heart rate variability, respiratory rate, electrocutaneous activity due to sweating, acceleration (shocks and vibrations to the body), etc., and the worker biometric information acquisition unit 15 can acquire this biometric information from the biometric information sensor 180. There are no particular limitations on the biometric information, as long as it is information that indicates the mental and physical state of the worker.

[0078] The worker biometric information storage unit 16 is a predetermined area of ​​the auxiliary storage unit 123 that stores the biometric information of the worker P acquired by the worker biometric information acquisition unit 15. The worker biometric information storage unit 16 stores the information in association with the target worker by the ID of the worker P, etc. The worker biometric information storage unit 16 also stores the worker biometric information in association with the time at which it was detected.

[0079] The machine profile information acquisition unit 17 acquires machine profile information, which is information specific to the machine. If the machine is a robot, the machine profile information includes, but is not limited to, specification information such as the model name, serial number, manufacturer name, and variable parameter setting range (maximum movement range, maximum operating speed, etc.), operation information such as the line name, manager, and defect history, performance information such as cumulative operating time, daily inspection records, maintenance records, and operating results, operation plans, machine capability information such as operating speeds and work defect rates calculated for each work object or work condition, and risk assessment information. The machine profile information may also include a production plan related to the machine. The machine profile information acquisition unit 17 can acquire this information from the production management device 110. Here, the machine profile information acquisition unit 17 corresponds to the production plan acquisition unit of the present invention.

[0080] The machine profile information storage unit 18 is a predetermined area of ​​the auxiliary storage unit 123 that stores the machine profile information acquired by the machine profile information acquisition unit 17 .

[0081] The machine operation information acquisition unit 19 acquires machine information related to the operation (operation) of the machine. The machine information includes the machine's operating status, parameter values ​​related to operation, whether or not a malfunction has occurred, and measurement data (vibration, electromagnetic waves, current value, voltage value, power value, power amount, temperature, etc.) obtained by sensors installed inside the machine. The machine information acquisition unit acquires the machine's operation, parameter values ​​related to operation, and whether or not a malfunction has occurred from the control device 130. The worker operation information acquisition unit 13 may analyze images captured by the image sensor 150 using the image analysis unit 23 described below and acquire the extracted machine operation. The measurement data may be acquired from a sensor installed outside the machine. The machine operation information acquisition unit 19 acquires this data in real time.

[0082] The machine operation information storage unit 20 is a predetermined area of ​​the auxiliary storage unit 123 that stores the machine operation information acquired by the machine operation information acquisition unit 19. The machine operation information storage unit 20 stores the machine operation information in association with the target machine using the machine ID or the like. The machine operation information storage unit 20 stores the machine operation information in association with time.

[0083] The working environment information acquisition unit 21 acquires working environment information related to the worker's working environment (ambient environment). As described above, working environment information includes, but is not limited to, temperature, humidity, illuminance, carbon monoxide concentration, amount of suspended dust, noise level, vibration level, risk assessment information, etc. The working environment information acquisition unit 21 acquires this working environment information from the working environment sensor 160. The working environment information also includes information such as the presence or absence of obstacles or other objects (including structures) in the working environment of the worker P, information about the location where the worker is engaged in production, such as production line A, and information about the work content of the work to be performed at that location. The working environment information acquisition unit 21 may analyze images captured by the image sensor 150 using the image analysis unit 23 described below to acquire information about the presence or absence of obstacles or other objects in the working environment.

[0084] The working environment information storage unit 22 is a predetermined area of ​​the auxiliary storage unit 123 that stores the working environment information acquired by the working environment information acquisition unit 21. The working environment information storage unit 22 stores the working environment information in association with the time of detection. The working environment information storage unit 22 stores the working environment information in association with the position or spatial range that is the target of the working environment information, depending on the installation location of the working environment sensor 160, etc. The working environment information storage unit 22 may also store work-related information by associating the working environment information with work.

[0085] Hereinafter, the above-mentioned worker profile information, worker movement information, and worker biometric information may be collectively referred to as worker information, and the machine profile information, machine movement information, and work environment information may be collectively referred to as environmental information. Worker information and environmental information are information that can be extracted as information about workers and machines within a certain time range and a certain area.

[0086] The image analysis unit 23 analyzes the image captured by the image sensor 150 and extracts information such as the movement of the worker P, the movement of the machine, and the presence or absence of obstacles around the worker P. Any appropriate method can be adopted for the recognition process of the worker P, the machine, objects around the worker P, etc.

[0087] The index generation unit 24 generates evaluation indexes for evaluating the production status. Here, a productivity index that evaluates the production status from the perspective of productivity and a safety and health index that evaluates the production status from the perspective of safety and health are generated based on worker profile information, worker motion information, worker biometric information, machine profile information, machine operation information, and work environment information. The productivity index and the safety and health index are generated by combining at least one of the worker profile information, worker motion information, worker biometric information, machine profile information, machine operation information, and work environment information, and further performing arithmetic processing on them.

[0088] (Human Productivity) Various indicators can be used as productivity evaluation indicators. For example, human productivity expressed by the following formula (1) can be used: Human Productivity = Output Man-hours ÷ Production Input Man-hours (1) Here, production input man-hours are working hours minus excluded man-hours, such as time spent supporting other departments or engaged in improvement activities, and output man-hours are direct man-hours minus work losses and defect losses. Furthermore, while human productivity is basically expressed by formula (1), it can also be expressed by formula (2) below, and can also be broken down and managed as in formula (2) below: Human Productivity = Work Efficiency × Production Efficiency (2) Here, work efficiency and production efficiency are indicators expressed by formulas (3) and (4), respectively: Work Efficiency = Output Man-hours ÷ Direct Man-hours (3) Production Efficiency = Direct Man-hours ÷ Production Input Man-hours (4) Here, direct man-hours are the time obtained by excluding indirect man-hours, such as setup time, and waiting time from production input man-hours. Furthermore, work efficiency and production efficiency, which are elements of human productivity, can also be used as productivity indicators.

[0089] FIG. 5 is a flowchart illustrating the procedure for calculating the work efficiency of process A. First, the measurement of man-hours for process A is started (step S11). Once the man-hour measurement has started, it is determined whether a work stoppage has occurred (step S12). Whether a work stoppage has occurred can be determined based on the machine operation information stored in the machine operation information storage unit 20 and the worker operation information stored in the worker operation information storage unit 14. The relationship between the machine operation and worker operation and the process can be determined based on the machine schedule stored in the machine profile information storage unit 18 and the worker schedule stored in the worker profile information. If a work stoppage has not occurred, proceed to step S16.

[0090] When a work stoppage occurs, measurement of the work stoppage time is started (step S13). After starting measurement of the work stoppage time, it is determined whether or not the work has been returned to (step S14). The determination of whether or not the work has been returned to is repeated at appropriate timing until the work has been returned to.

[0091] If the worker has returned to work, the measurement of the work stoppage time is terminated (step S15). Then, it is determined whether or not process A has been completed (step S16). If process A has not been completed, the process returns to step S12. If process A has been completed, the measurement of the man-hours for process A is terminated (step S17), and the work efficiency of process A is calculated and recorded in a predetermined area (step S18). Similarly, production efficiency can also be calculated, and human productivity can be calculated from the calculated work efficiency and production efficiency.

[0092] FIG. 6 shows an example of data such as the thus measured completed man-hours, direct man-hours, and production input man-hours.

[0093] For example, for the product of Lot No. xxxx1 on production line A on 20xx / yy / zz, the recorded values ​​are 1200 man-hours for completed work, 1140 man-hours for direct work, 1080 man-hours for production input, 60 for process 1, 40 for process 2, and 80 for process 3. The same data is recorded for the products of Lot No. xxxx2 and Lot No. xxxx3 on production line A on the same day.

[0094] (Overall Equipment Effectiveness) Alternatively, the overall equipment effectiveness (OEE) shown in the following formula (5) can be used as a productivity index. Overall equipment effectiveness = availability rate × performance rate × quality rate (5) Here, availability rate, performance rate, and quality rate are indexes shown in the following formulas (6), (7), and (8), respectively. Availability rate = operation time ÷ load time (6) Performance rate = net operation time ÷ operation time (7) Quality rate = value operation time ÷ net operation time (8) Here, load time is the time during operation time when a machine is actually turned on and a load is applied, net operation time is the time during operation time (the time during load time when a machine is actually operating) when the machine is able to perform at its intended performance, and value operation time is the time during net operation time when good products are produced. The quality rate can also be calculated using the following formula (9). Good yield rate = (processed quantity - defective quantity) / processed quantity (9) The load time and operating time can be calculated based on the machine operation information stored in the machine operation storage unit, and the good yield rate can be calculated from the machine operation information stored in the machine operation storage unit and information recorded in the performance management database of the production management device 110. In addition, the availability rate, performance rate, and good yield rate, which are elements of overall equipment effectiveness, can also be used as productivity indices.

[0095] FIG. 7 shows examples of data such as availability, performance rate, non-defective product rate, load time, operating time, and net operating time, which are the basis for calculating the overall equipment effectiveness.

[0096] For example, for production line A, on 20xx / yy / 01, the time availability rate was 0.9375, the performance availability rate was 0.9333, the non-defective product rate was 0.95, the load time was 8 hours, the operating time was 7.5 hours, and the net operating time was 7 hours. This data is also recorded for production line A on 20xx / yy / 01 and 20xx / yy / 02 in the same way.

[0097] (Safety and Health Indicators) Various indices can be used as basic data for the safety and health indicators, but the indices shown in FIG. 8 can be used as work environment indices related to the work environment of worker P. These work environment indices are detected by the work environment sensor 160, acquired by the work environment information acquisition unit 21, and recorded in association with the work; here, work environment indices related to work A are shown. The work environment indices are acquired at predetermined times (here, every hour) and recorded in chronological order. Here, example data of the work environment indices are shown, including temperature (°C), humidity (%), illuminance (lx), carbon monoxide concentration (ppm), suspended dust amount (mg / m3), and noise level (dB), but the work environment indices are not limited to these. For example, at 09:00:00 on 20xx / yy / zz, the temperature was recorded as 25.5°C, humidity as 67.1%, illuminance as 640 lx, carbon monoxide concentration as 0 ppm, suspended dust amount as 0.005 mg / m3, and noise level as 54 dB, and these data were also recorded at 10:00:00 and 11:00:00 on the same day.

[0098] Safety and health indicators or their basic data can be based on worker P's biometric information or indices calculated from biometric information. Examples of such safety and health indicators include stress, drowsiness / concentration, fatigue, and near-miss situations. These indicators can be calculated based on biometric information such as heart rate, heartbeat interval, and LF / HF. Here, LF / HF is the ratio of the frequency component (LF value) in the low-frequency (LF) band (0.004-0.15 Hz) to the frequency component (HF value) in the high-frequency (HF) band (0.15-0.4 Hz) of the power spectrum related to heartbeat interval fluctuations. This LF / HF ratio can be used as an indicator of stress. Near-miss situations can also be evaluated as a momentary increase in LF / HF.

[0099] FIG. 9 shows examples of biometric information or data based on biometric information. Any of these, a combination of these, or data that has undergone further computational processing can be used as safety indicators. Data related to each worker is recorded, and data related to worker ** is shown here. Worker biometric information related to safety and health indicators is acquired at predetermined intervals (here, every second) and recorded in chronological order. Examples of worker biometric information related to safety and health indicators include, but are not limited to, heart rate (bpm), heart rate interval (ms), LF / HF, drowsiness / concentration, stress, and years of experience. Data related to safety and health indicators that may change in real time is acquired at predetermined intervals (here, every second) and recorded in chronological order. Years of experience can be acquired whenever the data in the labor management database is updated. Furthermore, worker information related to safety and health indicators includes directly detectable data such as heart rate and heart rate interval, as well as data calculated based on detected data such as LF / HF, drowsiness / concentration, and stress. For example, at 09:00:00 on 20xx / yy / zz, worker A's heart rate is recorded as 80 bpm, heartbeat interval is recorded as 950 ms, sleepiness / concentration level is recorded as 20, stress is recorded as 30, and years of experience is recorded as 5 years, and these data are similarly recorded at 09:00:01 and 09:00:02 on the same day.

[0100] The generated productivity index and safety and health index are stored in the index storage unit 26 , which is a predetermined area of ​​the auxiliary storage unit 123 .

[0101] The production management support device 120 holds the worker profile information, worker operation information, worker biometric information, machine profile information, machine operation information, and work environment information, which are the data that form the basis of the productivity index and the safety and health index, as information associated with time. Therefore, the productivity index and the safety and health index can be generated as information associated with time.

[0102] FIG. 10 is a graph showing the time evolution of productivity indicators and safety and health indicators on the same time axis. The screen generation unit of the production management support device 120 can display such information on the display constituting the output unit 33. Here, the productivity indicators, such as performance rate and work efficiency, and the safety and health indicator, such as near-miss incidents, are displayed on the same time axis. By comparing and contrasting the productivity indicators and safety and health indicators on the same time axis, it is possible to clearly identify, from the area enclosed by the dashed dotted line, the occurrence of near-miss incidents by workers due to machine stoppages. Therefore, rather than simply examining the production situation from the perspective of productivity, it is possible to consider worker safety and health as well, thereby achieving a balance between improving productivity and optimizing safety and health for improvement.

[0103] The indices generated by the index generation unit 24 may be generated from specific basic data through specific combinations and calculations, but the contents of the productivity indexes and safety and health indexes may be changed depending on the purpose. For example, productivity indexes and safety and health indexes according to the purpose may be generated not only by replacing the basic data but also by changing the weighting of the basic data. As will be described later, machine control, worker work procedures, and production plans may be changed based on these productivity indexes and safety and health indexes. In this way, productivity information and safety and health indexes after changes to machine control, etc., can be obtained as feedback, and the productivity indexes and safety and health indexes may be updated by machine learning the selection of basic data for the productivity indexes and safety and health indexes and their weighting.

[0104] (Evaluation Index Generation Model) FIG. 11 shows the learning processing unit 241 that generates the evaluation index generation model of the index generation unit 24. The learning processing unit 241 receives input of the productivity index and safety and health index stored in the index storage unit 26, the worker profile information stored in the worker profile information storage unit 12, the worker movement information stored in the worker movement information storage unit 14, the worker biometric information stored in the worker biometric information storage unit 16, the machine profile information stored in the machine profile information storage unit 18, the machine movement information stored in the machine movement information storage unit 20, and the work environment information stored in the work environment information storage unit 22. The worker profile information, worker movement information, worker biometric information, machine profile information, machine movement information, and work environment information are associated as information within a certain time range and a certain area. The learning processing unit 241 trains a learning model 242 using all or part of this information as learning data to generate an index generation model 243 that generates productivity evaluation indexes and safety and health indexes. The learning processing unit 241 is realized by reading out a learning model generation program stored in a predetermined area of ​​the auxiliary storage unit 123 and executing it in the processor 121. The generated index generation model 243 is stored in a predetermined area of ​​the auxiliary storage unit 123 and is used to generate productivity indexes and safety and health indexes in the index generation unit 24.

[0105] The simulation unit 29 simulates changes to the machines and work procedures when the production status evaluation unit 25 evaluates that there is a predetermined change. The simulation will be described in detail later.

[0106] The countermeasure decision unit 30 decides on a change to the machine and / or a change to the work procedure as a countermeasure based on the results of the simulation in the simulation unit 29 .

[0107] The improvement information generation unit 31 generates information for improvement such as control commands for the machine, updated information for work procedures and manuals, work instructions for workers, etc. in accordance with the countermeasures. The control commands for the machine are sent from the output unit 42 to the control device 130, and the work procedures and the like are notified in a predetermined manner and used for training workers, etc., and work instructions are given in accordance with the updated work procedures.

[0108] 12 and 13 are flowcharts illustrating a process that includes generating productivity indexes and safety and health indexes and determining countermeasures. Fig. 12 shows a process that starts from a change in a safety and health index, and Fig. 13 shows a process that starts from a change in a productivity index. The process that starts from generating productivity indexes and safety and health indexes and determines countermeasures described here corresponds to the production management support method of the present invention.

[0109] In FIG. 12, first, the index generating unit 24 acquires worker information and environmental information (step S21).

[0110] Next, the index generating unit 24 generates a productivity index and a safety and health index based on the worker information and the environmental information (step S22).

[0111] The production status evaluation unit 25 determines whether or not an incident has occurred based on the safety and health index information (step S23). The presence or absence of an incident is determined by comparing the safety and health index with a threshold set for the relevant safety and health index stored in the evaluation criteria information storage unit 27. Examples of incidents include, but are not limited to, the occurrence of near misses and the detection of excessive stress. The table in which the thresholds are registered may be switched depending on the target worker, machine, and working environment. Alternatively, the occurrence or absence of an incident may be determined using an incident determination model that has learned productivity indexes, safety and health indexes, worker and environmental information.

[0112] The determination of whether or not there is an incident is repeated for the safety and health indicators generated from the acquired worker information and environmental information. If there are no incidents for the safety and health indicators generated from the acquired worker information and environmental information, the process ends.

[0113] If an incident is determined to have occurred, the production status evaluation unit 25 checks the productivity index at the time of the incident (step S24) and determines whether the cause of the incident is the machine or the worker (step S25). The production status evaluation unit can determine the cause of the incident based on a predetermined incident cause determination table stored in the evaluation criteria information storage unit 27. An incident cause determination model generated by learning incident cases and cause determination results may be stored in the evaluation criteria information storage unit 27, and the production status evaluation unit 25 may determine the cause of the incident based on the incident cause determination model. For example, an incident may occur due to a decrease in the productivity index, such as when a worker approaches a hazard without stopping the machine during a brief stop, and the machine suddenly starts moving again when the brief stop is lifted. Incidents may also occur without a decrease in the productivity index, such as when a worker makes a mistake in their work and acts in a panic, resulting in a near miss with a hazard. Furthermore, a decrease in the productivity index may occur due to the occurrence of an incident, such as when a high level of fatigue or stress in a specific task reduces the productivity index.

[0114] The production status evaluation unit 25 determines the cause of the incident based on the safety and health index and the productivity index, extracts countermeasures based on the countermeasure table, and performs a simulation, which will be described later. The countermeasure table is stored in the evaluation criteria information storage unit 27.

[0115] Various variations are possible in extracting countermeasures based on productivity indicators and safety and health indicators, and several examples are described below. Countermeasures include issuing control commands to machines, updating work procedures and manuals, issuing work instructions to workers, and changing production plans. Furthermore, the worker property information recorded in the production management device 110 or the labor management database includes information on capabilities such as work speed, which is the basis for production planning. Therefore, this information may be updated or added as new worker-specific information as a countermeasure. Similarly, the machine property information recorded in the production management device 110 includes information on machine capabilities such as operating speed, which is the basis for production planning. Therefore, this information may be updated or added as machine-specific information as a countermeasure.

[0116] If the worker's stress level is still low while waiting for work and the machine's operating speed is still sufficient, countermeasures can be proposed, such as instructing the worker to work at a faster speed and instructing the machine to speed up its processing speed.If the worker's performance is declining at this time, countermeasures can be proposed, such as adding support and assistance from other workers or reviewing the worker's allocation.

[0117] In addition, if a worker's stress exceeds a threshold and is placing a burden on the worker, or if temperature, noise, etc. exceed a threshold, changes or improvements to machinery can be suggested as countermeasures.

[0118] Furthermore, if a worker is detected to be startled, the system can determine the impact on production from the state of the machine based on the worker's location and work content at the time, and suggest countermeasures such as instructing changes to the operating speed, direction, timing, etc. of the machine that caused the worker's startle.

[0119] The table for determining such countermeasures may be switched based on the type of worker, such as the worker's proficiency level, physique, dominant hand, average movement speed, average heart rate, etc., or the type of machine, such as the type, manufacturer, size, amount of power used, and period of use.

[0120] In step S25, if the machine change process simulation unit 51 determines that the cause of the incident is the machine, it performs a simulation (step S26) of the process when changes are made to the machine extracted as a countermeasure.

[0121] 14 is a block diagram showing the configuration of the simulation unit 29 of the production management support device 120. The simulation unit 29 includes a machine change process simulation unit 51, a machine risk assessment simulation unit 52, a work procedure change process simulation unit 53, and a worker risk assessment simulation unit 54.

[0122] The machine change process simulation unit 51 performs a simulation to estimate the effect of implementing the countermeasure extracted by the production status evaluation unit 25. The effect of this countermeasure can be determined, for example, by the proximity to a predetermined target value set for a productivity index and / or a safety and health index.

[0123] The machine change process simulation unit 51 determines the correction instructions (i.e., control contents) to be applied to the machine based on the estimated effect. If it is estimated that the desired effect will not be obtained, the production status evaluation unit 25 extracts another countermeasure and performs the machine change process simulation again.

[0124] The control of the machine may include changing parameter settings, changing target value settings, changing operation modes, correcting production plans, issuing replanning commands, or changing warning conditions.

[0125] Returning to the description of Fig. 12, following step S26, the machine risk assessment simulation unit 52 performs a simulation of risk assessment when a change is made to the machine (step S27).

[0126] The machine risk assessment simulation unit 52 simulates the risk when the control content determined by the machine change process simulation unit 51 is applied to the machine.

[0127] The machine risk assessment simulation unit 52 includes a hazard identification unit 531 , a risk estimation unit 532 , a risk evaluation unit 533 , and a risk reduction processing unit 534 .

[0128] The hazard identification unit 521 acquires work environment information and hazard data, identifies hazards, and specifies hazard areas. For example, the hazard identification unit 521 identifies elements having driving sources, such as a robot arm, an end effector, and a peripheral conveyor, as hazards. The hazard identification unit 521 also determines whether the movement range of the hazard overlaps with the movement range of the worker. The hazard identification unit 521 determines an area where the movement range of the hazard overlaps with the movement range of the worker as a hazard area. The hazard identification unit 521 determines, from the movement range of the hazard sources, whether there is a risk of a human body being trapped between two hazard sources. The hazard identification unit 521 determines, from the movement range of the hazard sources, whether there is a risk of a human body being trapped between a hazard source and a surrounding object such as a desk, wall, pillar, or door. The hazard identification unit 521 also determines an area where a human body may be trapped as a hazard area. The method for identifying the source of danger and specifying the dangerous area is not limited to the above-described method.

[0129] The risk estimation unit 522 performs risk estimation. The risk estimation unit 522 identifies possible hazardous events for the identified hazard sources and specified hazardous areas by risk estimation. The risk estimation unit 522 also identifies injuries to the human body that may occur as a result of the possible hazardous events.

[0130] When the worker's range of motion and the robot arm's range of motion overlap, a "collision with the robot arm" is listed as a hazardous event. When the distance between the worker's range of motion and the robot arm's range of motion is narrower than a specified distance, the worker may collide with the robot arm. In this case, a "collision with the robot arm" is also listed as a hazardous event. When the distance between the robot arm's range of motion and a surrounding object is narrower than a specified distance, a "getting caught between the robot arm and a surrounding object" is listed as a hazardous event. When the hazardous event is a "collision with the robot arm," an injury may be a "bruise or fracture." When the hazardous event is a "getting caught between the robot arm and a surrounding object," an injury may be a "fracture or more serious injury." The risk estimation unit 522 determines these hazardous events and these injuries based on the hazard data. The risk estimation unit 522 performs risk estimation using factors such as the severity of the injury, the frequency of exposure, and the possibility of avoiding the harm.

[0131] The risk estimation unit 522 can employ an appropriate method for risk estimation, such as a risk graph method using a tree diagram, a risk matrix method using a table, a numerical scoring method that adds and multiplies values, or a hybrid tool method that combines these.

[0132] The risk estimation unit 522 determines a risk level (e.g., six levels) that indicates the degree of harm based on, for example, the severity of the injury, the frequency of exposure, and the possibility of avoidance. For example, risk level 1 may be the least severe of the six risk levels, and risk level 6 may be the most severe of the six risk levels.

[0133] The risk assessment unit 523 performs risk assessment. The risk assessment unit 523 evaluates whether the risk resulting from the risk estimation is an acceptable risk. The risk assessment unit 523 evaluates the risk by comparing the risk level determination result resulting from the risk estimation with a threshold. For example, if the value indicating the risk level is equal to or less than the threshold, the risk assessment unit 523 evaluates the risk as an acceptable risk. On the other hand, if the value indicating the risk level is greater than the threshold, the risk assessment unit 523 evaluates the risk as an unacceptable risk.

[0134] If the risk is an unacceptable risk, the worker risk assessment simulation unit 54 instructs the risk reduction processing unit 524 to perform risk reduction processing for the risk assessed as an unacceptable risk.

[0135] The risk reduction processing unit 524 determines whether or not risk reduction is possible for a risk that has been determined to be unacceptable. The risk reduction processing unit 524 acquires safety device data. The risk reduction processing unit 524 determines whether or not risk reduction is possible by using the safety device 200 based on the safety device data.

[0136] The risk reduction processing unit 524 determines whether risk reduction is possible through safeguarding and additional protection measures based on the safety device data. If it is determined that risk reduction is possible through either measure, the risk reduction processing unit 524 determines that risk reduction is possible, and if it is determined that risk reduction is impossible through either measure, it determines that risk reduction is impossible and terminates the risk reduction processing.

[0137] If the risk reduction processing unit 524 determines that risk reduction is not possible, the production status evaluation unit 25 extracts another countermeasure, and the simulation unit 29 instructs the machine change process simulation unit 51 to simulate the new countermeasure, and the same process is repeated until risk reduction is possible.

[0138] If the risk assessment unit 533 assesses the risk as an acceptable risk, or if the risk reduction processing unit 534 determines that the risk can be reduced, the countermeasure determination unit 30 determines each measure as a countermeasure based on the machine risk assessment simulation (step S28).

[0139] Based on the countermeasure determined in step S28, the improvement information generating unit 31 generates a control command and reflects the countermeasure in the machine (step S29).

[0140] Then, the process proceeds to step S33, where the improvement information generating unit 31 reflects the countermeasure decided in step S29 in the work procedure, for example, by updating the work procedure manual in accordance with the countermeasure.

[0141] If it is determined in step S25 that the cause of the incident is the worker, the work procedure change process simulation unit 53 performs a simulation (step S30) of the process when a change is made to the worker's work procedure.

[0142] The work procedure change process simulation unit 53 performs a simulation to estimate the effect of implementing the countermeasures extracted by the production status evaluation unit 25. The effect of these countermeasures can be determined, for example, by the proximity to predetermined target values ​​set for productivity indicators and / or safety and health indicators.

[0143] Returning to the description of Fig. 12, following step S30, the worker risk assessment simulation unit 54 performs a simulation of risk assessment when a change is made to the worker's work procedure (step S31).

[0144] The worker risk assessment simulation unit 54 simulates the risk when the instruction content determined by the work procedure change process simulation unit 53 is applied to the worker P.

[0145] The worker risk assessment simulation unit 54 includes a hazard identification unit 541, a risk estimation unit 542, a risk assessment unit 543, and a risk reduction processing unit 544. The functions of the hazard identification unit 541, the risk estimation unit 542, the risk assessment unit 543, and the risk reduction processing unit 544 are similar to those of the hazard identification unit 521, the risk estimation unit 522, the risk assessment unit 523, and the risk reduction processing unit 524 of the machine risk assessment simulation unit 52, and therefore description thereof will be omitted.

[0146] If the risk assessment unit 543 assesses the risk as an acceptable risk, or if the risk reduction processing unit 544 determines that the risk can be reduced, the countermeasure determination unit 30 determines each measure as a countermeasure based on the worker risk assessment simulation (step S32).

[0147] Then, the countermeasures decided in step S32 are reflected in the work procedure (step S33).

[0148] Then, the improvement information generating unit 31 issues instructions to educate workers about the updated work procedures and gives instructions based on the updated work procedures (step S34).

[0149] Referring to FIG. 13, a process including the generation of productivity indexes and safety and health indexes and the determination of countermeasures, starting from a change in productivity index, will be described.

[0150] First, the index generating unit 24 acquires worker information and environmental information from each storage unit 12, etc. (step S41).

[0151] Next, the index generating unit 24 generates a productivity index and a safety and health index based on the worker information and the environmental information (step S42).

[0152] The presence or absence of a fluctuation in the productivity index is determined based on the environmental information (step S43). The presence or absence of a fluctuation in the productivity index is determined by comparing the productivity index with a threshold set for the productivity index stored in the evaluation criteria information storage unit 27. The table in which the thresholds are registered may be switched depending on the target worker, machine, and working environment. Furthermore, the occurrence or absence of an incident may be determined using a determination model that has learned the productivity index, safety and health index, worker, and environmental information.

[0153] The determination of whether or not there is a change in the productivity index is repeated for the acquired range of worker information and environmental information. If there is no change in the productivity index for the acquired range of worker information and environmental information, the process ends.

[0154] If it is determined that there is a fluctuation in the productivity index, it is determined whether the cause of the productivity index fluctuation is the machine or the worker (step S44). For example, it is determined whether the cause is the machine, such as when the productivity index fluctuation is due to a machine malfunction, or whether the cause is the worker, such as when the productivity index fluctuation is due to variations in man-hours caused by differences in the worker's years of experience, proficiency, etc. The cause of the productivity index fluctuation can be determined by storing a predetermined productivity index fluctuation factor judgment table in the evaluation criterion information storage unit 27 and making a judgment based on this productivity index factor judgment table. A productivity index fluctuation factor judgment model generated by learning productivity index fluctuation occurrence cases and factor judgment results may be stored in the evaluation criterion information storage unit 27, and the production status evaluation unit 25 may make a judgment based on this productivity index fluctuation factor judgment model. The processing from steps S44 to S53 is the same as the processing from steps S25 to S34 shown in FIG. 12, so detailed description thereof will be omitted.

[0155] The production status evaluation unit 25 determines the cause of the productivity index fluctuation, extracts countermeasures based on the countermeasure table, and performs a simulation, which will be described later. The countermeasure table is stored in the evaluation criterion information storage unit 27.

[0156] If it is determined in step S44 that the cause of the productivity index fluctuation is a machine, the machine change process simulation unit 51 simulates the process when changes are made to the machine extracted as a countermeasure (step S45).

[0157] Then, the machine risk assessment simulation unit 52 performs a simulation of risk assessment when a change is made to the machine (step S46).

[0158] Countermeasures are determined based on the worker risk assessment simulation (step S47).

[0159] The measures determined in step S47 are reflected in the machine (step S48).

[0160] If it is determined in step S44 that the cause of the productivity index fluctuation is the worker, the work procedure change process simulation unit 53 simulates the process when a change is made to the worker's work procedure (step S49).

[0161] Then, the worker risk assessment simulation unit 54 performs a simulation of risk assessment when a change is made to the worker's work procedure (step S50).

[0162] Countermeasures are determined based on the worker risk assessment simulation (step S51).

[0163] Then, the countermeasures decided in step S51 are reflected in the work procedure (step S52).

[0164] Then, instructions are given to educate workers about the updated work procedures and instructions are given based on the updated work procedures (step S53).

[0165] In this way, the production management support device 120 enables production management that takes into consideration not only productivity but also safety and health by using productivity indices that evaluate productivity and safety and health indices that evaluate safety and health. Furthermore, it also becomes possible to optimize production management through the productivity indices and safety and health indices.

[0166] In addition, the components of the present disclosure are described below with the reference numerals in the drawings to enable comparison between the components of the present disclosure and the configurations of the embodiments. <Supplementary Note 1> A production management support device (120) comprising: a worker information acquisition unit (11, 13, 15) that acquires worker information related to workers (P) engaged in production; an environmental information acquisition unit (17, 19, 21) that acquires environmental information related to the environment, including machines (140) used in the production; and an index generation unit (24) that generates a safety and health index related to safety and a productivity index related to productivity based on the acquired worker information and environmental information. <Supplementary Note 2> The production management support device (120) described in Supplementary Note 1 is characterized in that the index generation unit (24) generates the safety and health index and the productivity index based on the acquired worker information and environmental information using a learning model (243) that has been machine-learned to acquire the worker information, the environmental information, the safety and health index, and the productivity index. <Supplementary Note 3> The production management support device (120) according to Supplementary Note 2, characterized in that the learning model (120) is a learning model (120) that has been machine-learned using worker information related to the worker and environmental information related to the environment within a certain time range and a certain area. <Supplementary Note 4> The production management support device (120) according to any one of Supplements 1 to 3, characterized in that the worker information includes biometric information of the worker. <Supplementary Note 5> The production management support device (120) according to any one of Supplements 1 to 4, characterized in that the worker information includes worker profile information of the worker. <Supplementary Note 6> The production management support device (120) according to any one of Supplements 1 to 5, characterized in that the environmental information includes machine profile information of the machine. <Supplementary Note 7> The production management support device (120) described in any one of Supplementary Notes 1 to 6, characterized in that the worker information acquisition unit (11, 13, 15) acquires time information associated with the worker information and the environmental information, and the environmental information acquisition unit (17, 19, 21) acquires location information associated with the worker and the environmental information.<Supplementary Note 8> The production management support device (120) according to any one of Supplements 1 to 7, further comprising: a screen generation unit (24) that generates a screen displaying changes over time in the safety and health index and the productivity index. <Supplementary Note 9> The production management support device according to Supplementary Note 8, further comprising: a screen generation unit (24) that generates a screen displaying the safety and health index and the productivity index in association with a time axis. <Supplementary Note 10> The production management support device (120) according to any one of Supplements 1 to 9, further comprising: a countermeasure determination unit (30) that determines a countermeasure to ensure the safety of the worker based on the safety and health index and the productivity index generated by the index generation unit (24). <Supplementary Note 11> The production management support device (120) according to Supplementary Note 10, further comprising: a safety and health target receiving unit (32) that receives input of a target for the safety and health index; and a productivity target receiving unit (32) that receives input of a target for the productivity index, wherein the countermeasure determination unit (30) determines the countermeasure based on the received target for the safety and health index and the target for the productivity index. <Supplementary Note 12> The production management support device (12) according to Supplementary Note 10 or 11, further comprising a production plan acquisition unit (11, 17) that acquires a production plan including the worker and the machine, and the countermeasure includes changing the production plan. <Supplementary Note 13> The production management support device (120) according to any one of Supplements 10 to 12, characterized in that the worker information includes worker profile information of the worker, and the countermeasure includes updating or adding the worker profile information. <Supplementary Note 14> The production management support device (120) according to any one of Supplements 10 to 13, characterized in that the countermeasure includes at least one of information about the worker and information about the machine. <Supplementary Note 15> The production management support device (120) according to any one of Supplements 1 to 14, further comprising a worker information detection unit (130, 150) that detects the worker information. <Supplementary Note 16> The production management support device (120) according to any one of Supplementary Notes 1 to 15, characterized in that it comprises an environmental information detection unit (150, 180) that detects the environmental information.<Supplementary Note 17> A production management support method comprising the steps of: acquiring worker information about workers engaged in production (S21, S41); acquiring environmental information about the environment including machines used in the production (S21, S41); generating a safety and health index related to safety and health based on at least the worker information (S22, S42); and generating a productivity index related to productivity based on at least the environmental information (S22, S42). <Supplementary Note 18> The production management support method according to Supplementary Note 17, comprising the step of determining countermeasures to ensure the safety of the workers based on the safety and health index and the productivity index (S32, S51). <Supplementary Note 19> A production management support program causing a computer to execute the production management support method according to Supplementary Note 17 or 18.

[0167] DESCRIPTION OF SYMBOLS 11: Worker profile information acquisition unit 13: Worker operation information acquisition unit 15: Worker biological information acquisition unit 17: Machine profile information acquisition unit 19: Machine operation information acquisition unit 21: Work environment information storage unit 24: Index generation unit 120: Production management support device

Claims

1. A production management support device comprising: a worker information acquisition unit that acquires worker information related to workers engaged in production; an environmental information acquisition unit that acquires environmental information related to the environment, including machines used in the production; and an index generation unit that generates a safety and health index related to safety and a productivity index related to productivity based on the acquired worker information and environmental information.

2. The production management support device of claim 1, characterized in that the index generation unit generates the safety and health index and the productivity index based on the acquired worker information and environmental information using a learning model that has been machine-learned to the worker information, the environmental information, the safety and health index, and the productivity index.

3. The production management support device of claim 2, characterized in that the learning model is a learning model that has been machine-learned to learn the worker information related to the worker and the environmental information related to the environment within a certain time range and a certain area.

4. A production management support device as claimed in claim 1 or 2, characterized in that the worker information includes biometric information of the worker.

5. A production management support device as described in claim 1 or 2, characterized in that the worker information includes worker profile information of the worker.

6. A production management support device according to claim 1 or 2, characterized in that the environmental information includes machine profile information of the machine.

7. A production management support device as described in claim 1 or 2, characterized in that the worker information acquisition unit acquires time information associated with the worker information and the environmental information, and the environmental information acquisition unit acquires location information associated with the worker and the environmental information.

8. A production management support device according to claim 1 or 2, further comprising a screen generating unit for generating a screen displaying the time-dependent changes of said safety and health indexes and said productivity indexes.

9. The production management support device according to claim 8, characterized in that the screen generation unit generates a screen that displays the safety and health index and the productivity index in correspondence with a time axis.

10. A production management support device as described in claim 1 or 2, characterized in that it is provided with a countermeasure decision-making unit that decides countermeasures to ensure the safety of workers based on the safety and health indexes and productivity indexes generated in the index generation unit.

11. A production management support device as described in claim 10, further comprising a safety and health goal receiving unit that receives input of the safety and health index goals, and a productivity goal receiving unit that receives input of the productivity index goals, wherein the countermeasure decision unit decides on the countermeasure based on the received safety and health index goals and productivity index goals.

12. The production management support device according to claim 10, further comprising a production plan acquisition unit that acquires a production plan including the workers and the machines, wherein the countermeasure includes a change to the production plan.

13. The production management support device according to claim 10, wherein the worker information includes worker profile information of the worker, and the countermeasure includes updating or adding to the worker profile information.

14. A production management support device according to claim 10, characterized in that the countermeasures include at least one of information for the worker and information for the machine.

15. The production management support device according to claim 1 or 2, characterized in that it is provided with a worker information detection unit that detects the worker information.

16. The production management support device according to claim 1 or 2, characterized in that it is provided with an environmental information detection unit that detects the environmental information.

17. A production management support method comprising: a step of acquiring worker information regarding workers engaged in production; a step of acquiring environmental information regarding the environment including machines used in the production; a step of generating a safety and health index regarding safety and health based at least on the worker information; and a step of generating a productivity index regarding productivity based at least on the environmental information.

18. The production management support method according to claim 17, further comprising a step of determining measures to ensure the safety of said workers based on said safety and health indicators and said productivity indicators.

19. A production management support program for causing a computer to execute the production management support method according to claim 17 or 18.

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