Work analysis system and work analysis method

Abstract

This work analysis system comprises: a work measurement unit 210 that collects measurement data from sensors which measure an operation of a worker with regard to prescribed work; a measurement data accumulation unit 120 that accumulates the measurement data collected by the work measurement unit; and a measurement data analysis unit 220 that analyzes the prescribed work on the basis of the measurement data accumulated in the measurement data accumulation unit. The measurement data analysis unit includes: a workability evaluation unit 221 that evaluates the workability of the prescribed work on the basis of the measurement data accumulated in the measurement data accumulation unit; and a work improvement approach determination unit 222 that determines a work improvement approach to the prescribed work on the basis of the workability of the prescribed work evaluated by the workability evaluation unit.

Description

[Technical Field]

[0001] The present invention relates to a work analysis system and a work analysis method. [Background technology]

[0002] To improve the productivity of semiconductor devices, semiconductor manufacturing equipment must not only improve its performance, but also improve its operating rate through more efficient maintenance. Much of the maintenance of semiconductor manufacturing equipment involves manual work. With labor shortages and a shortage of skilled workers, improving the efficiency of maintenance is an important issue.

[0003] Patent Document 1 discloses a learning support system that enables efficient acquisition of skills. The learning support system includes a display unit worn by the learner, an imaging unit worn by the learner to capture a visual field video of the learner, and a storage unit that stores a model video, which is a video of an instructor's work movements that serves as a model for the learner's movements. The model video is displayed on the display unit superimposed on the visual field video captured by the imaging unit, and the display content of the model video is dynamically changed according to the characteristics of the learner's work movements contained in the visual field video. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-144233 Summary of the Invention [Problem to be solved by the invention]

[0005] Many ideas are being considered to use sensors to monitor and support manual work, such as in Patent Document 1. While these ideas are expected to improve the efficiency of manual work, they are premised on the fact that the work is being done by hand, and cannot advance improvements beyond the limits of human ability.

[0006] To address the expected worsening shortage of labor and skilled workers, it is essential to review the current work or the structure of the equipment (product) that the work targets, from the perspective of whether the work is necessary in the first place, or whether it is appropriate to perform the work manually, and to achieve overall optimization.The purpose of the present invention is to provide a work analysis system and work analysis method that provide a perspective for work improvement based on work monitoring data obtained by sensors. [Means for solving the problem]

[0007] A work analysis system according to one embodiment of the present invention includes a work measurement unit that collects measurement data from a sensor that measures the movements of a worker in a predetermined work, a measurement data storage unit that stores the measurement data collected by the work measurement unit, and a measurement data analysis unit that analyzes the predetermined work based on the measurement data stored in the measurement data storage unit. The measurement data analysis unit calculates the workability of the predetermined work based on the measurement data stored in the measurement data storage unit. The complexity indicates the complexity of a given task, and the dexterity indicates the degree of experience and knowledge required to perform a given task. Workability evaluation section The workability evaluation unit performs a complexity evaluation based on the work time required for the work elements constituting the worker's movements included in the process constituting the predetermined work and the ease of the work elements, and the workability evaluation unit performs a dexterity evaluation based on the variance in the work time, the variance in the movements and the success rate of the work for each group of worker movements included in the process. . [Effects of the Invention]

[0008] The present invention provides a work analysis system and a work analysis method that provide insights into work improvement based on work monitoring data obtained by sensors. Other objects and novel features will become apparent from the description of this specification and the accompanying drawings. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic configuration diagram of a work analysis system. [Figure 2A] 1 shows a hardware configuration of an information processing device. [Figure 2B] FIG. 2 is a functional block diagram of the work analysis system. [Figure 3] 1 shows the flow of maintenance work for semiconductor manufacturing equipment. [Figure 4] FIG. 2 is a diagram for explaining the work performed in each step of the maintenance work. [Figure 5A] This is an example of complexity assessment of the equipment disassembly process. [Figure 5B] This is an example of evaluating the complexity of a maintenance process. [Figure 6] 10 is an example of a workability evaluation table. [Figure 7] 10 is a histogram of work time. [Figure 8A] This is an example of skill evaluation for the device dismantling process. [Figure 8B] This is an example of a maintenance process skill evaluation. [Figure 9] This is the decision flow for work improvement approaches. [Figure 10] 10 is an example of a work element conversion table. [Figure 11] This is a flow chart for creating a proposal for process automation. [Figure 12] 1 is an example of a machine function conversion table. [Figure 13] 10 is an example of a work analysis report display screen. DETAILED DESCRIPTION OF THE INVENTION

[0010] FIG. 1 shows a schematic configuration diagram of a work analysis system. Here, the process of monitoring a worker's work, analyzing the work, and proposing improvements using the work analysis system of this embodiment will be described using an example of a worker performing maintenance work on semiconductor manufacturing equipment 100. The work analysis system includes sensors 101-106 that monitor the worker's work on semiconductor manufacturing equipment 100, a measurement data collection device 110 that collects measurement data detected by sensors 101-106 regarding the worker's movements during work, a measurement data storage unit 120 that stores the measurement data collected by measurement data collection device 110, and a work analysis device 140 that analyzes the work and proposes improvements based on the measurement data stored in measurement data storage unit 120. The measurement data collection device 110, the measurement data storage unit 120, and the work analysis device 140 are connected to each other via a network 130 so that they can communicate with each other. The network 130 may be wired or wireless, and any communication standard may be used.

[0011] FIG. 2A shows the hardware configuration of the measurement data collection device 110 and the work analysis device 140. These are realized by an information processing device including, as shown in FIG. 2A, a processor (CPU) 201, a memory 202, a storage device 203, an input interface (I / F) 204, an output I / F 205, a communication I / F 206, and a bus 207 as main components. The processor 201 functions as a functional unit (functional block) that provides a predetermined function by executing processing in accordance with a program loaded into the memory 202. The storage device 203 stores data and programs used by the functional unit. The storage device 203 may be a non-volatile storage medium such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The input I / F 204 is an interface for connecting an input device 208 such as a keyboard or pointing device, and the output I / F 205 is an interface for connecting a display device 209. The communication I / F 206 enables communication with other information processing devices via the network 130. These are communicatively connected to each other via a bus 207 .

[0012] It should be noted that the measurement data collection device 110 and the work analysis device 140 do not need to be implemented as separate information processing devices, and they may be implemented on a single information processing device, with the measurement data stored in the storage device 203. In this case, the storage device 203 functions as the measurement data storage unit 120. Some or all of the functions of the measurement data collection device 110 and the work analysis device 140 may be implemented as applications on the cloud.

[0013] FIG. 2B shows a functional block diagram of the work analysis system. The work measurement unit 210 of the measurement data collection device 110 controls the sensors and accumulates the measurement data in the measurement data accumulation unit 120. As shown in FIG. 1, the sensors include a camera 101 that captures an overhead image of the worker's work, a device-mounted camera 102, an HMD (Head Mounted Display) 103, a workwear sensor 104 and a glove-type sensor 105 worn by the worker, and a 360° camera 106 that captures the entire work area. However, the sensors shown in FIG. 1 are merely examples, and sensors other than those illustrated may be used, and the illustrated sensors do not necessarily have to be used. The work measurement unit 210 monitors the state of the worker's work using these sensors. As a result, for example, RGB (color) data (video data) is obtained from the camera; if an RGBD camera, which is a sensor that can acquire the distance to the object in addition to RGB data, is used as the camera, ranging data indicating the distance to the object in addition to the video data is also obtained; data on the movement of the worker's line of sight from the HMD 103; data on the movement of the worker's skeleton from the workwear sensor 104; and data on the movement of the worker's fingers from the glove sensor 105 are all stored as measurement data in the measurement data storage unit 120. It is desirable to record all measurement data stored in the measurement data storage unit 120 with a timestamp (time information) based on the same reference time. This allows the work analysis device 140 to integrate measurement data from multiple sensors when analyzing the measurement data and analyze the work.

[0014] The measurement data analysis unit 220 of the work analysis device 140 analyzes the work using the measurement data accumulated in the measurement data accumulation unit 120. The work analysis results by the measurement data analysis unit 220 are displayed on the display device 209 of the work analysis device 140 by the analysis result output unit 225. The processing by the measurement data analysis unit 220 will be described later with reference to specific examples.

[0015] First, a specific example of maintenance work for semiconductor manufacturing equipment will be described with reference to Figures 3 and 4. The maintenance work is a work example in which semiconductor manufacturing equipment is disassembled, maintenance is performed on the target units, and then the parts are reassembled to return the equipment to an operational state, and as shown in Figure 3, it is assumed to consist of three main steps: equipment disassembly (S01), maintenance of the target units (S02), and part assembly (S03). Note that this specific example is provided to specifically explain the processing in this embodiment, and the analysis targets of the work analysis system and work analysis method of this embodiment are not limited to this example.

[0016] The work performed in each step will be explained using Figure 4. In Figure 4, the semiconductor manufacturing equipment is shown schematically as comprising a main body 401, an upper unit 402, and a lower unit 403. For example, if the semiconductor manufacturing equipment is a plasma processing apparatus, the upper unit 402 is a cavity chamber for generating plasma, and the lower unit 403 is a vacuum vessel on which a sample to be processed is placed. In this specific example, the maintenance location is the lower unit 403.

[0017] The device disassembly process (S01) corresponds to states S11 to S14. State S11 is the state at the start of work, and state S12 is the state in which upper unit 402 and lower unit 403, which are connected to each other, have been separated from main body 401. State S13 shows a state in which screws are being manually removed using a tool such as a screwdriver to separate upper unit 402 from lower unit 403. State S14 is the state in which upper unit 402 has been separated from lower unit 403, making it possible to work on lower unit 403.

[0018] The maintenance process (S02) corresponds to states S21 to S23. State S21 shows the state in which the O-ring 405, which is a consumable item, is removed from the lower unit 403. State S22 shows the state in which the lower unit 403 is cleaned by wiping off any deposits or dirt on the lower unit 403. State S23 shows the state in which the consumable item is replaced and a new O-ring 406 is attached.

[0019] The parts assembly process (S03) corresponds to states S31 to S33. In state S31, the separated upper unit 402 is aligned with the lower unit 403, and in state S32, the screws are tightened to connect them. In state S33, the connected upper unit 402 and lower unit 403 are attached to the main body 401, thereby completing the assembly work.

[0020] Using the maintenance work described above as an example, we will explain the analysis of measurement data by the measurement data analysis unit 220. The measurement data analysis unit 220 has a workability evaluation unit 221, a work improvement approach determination unit 222, and a work improvement approach display generation unit 223 (see FIG. 2B). First, the workability evaluation unit 221 evaluates workability using the measurement data.

[0021] The workability evaluation unit 221 evaluates tasks from the perspectives of complexity and dexterity. To ensure the objectivity of the evaluation, the evaluation uses collected measurement data and calculates a quantitative evaluation value according to predetermined indicators. Here, task complexity refers to the complexity of the task performed by the worker, and indicators include, for example, the number of task elements that make up the task, the ease of performing the task elements, and the length of the task. On the other hand, task dexterity refers to the level of experience and knowledge required for the worker to complete the task. Specifically, tasks for which there is almost no difference in task efficiency or quality between skilled and unskilled workers are evaluated as low dexterity, while tasks for which there is a significant difference in task efficiency or quality between skilled and unskilled workers are evaluated as high dexterity. For example, indicators include the variance (variance) of task time between workers, the variance in tasks between skilled and unskilled workers, the percentage of task elements evaluated as high dexterity, and the task success rate.

[0022] For both task complexity and dexterity evaluations, it is desirable to monitor the tasks being analyzed by many workers over many times and accumulate measurement data, as the more measurement data is accumulated, the more statistically accurate and reliable the evaluation will be.

[0023] First, the workability evaluation unit 221 performs preprocessing to separate the measurement data into process units. For example, in this example, the measurement data is separated into three processes: an equipment disassembly process S01, a maintenance process S02, and a part assembly process S03. This separation can be achieved by performing video analysis on video data capturing the work process, recognizing distinctive objects and tasks, and identifying the timing at which the processes separate. For example, image recognition can be used to capture the timing at which the upper unit 402 and the lower unit 403 separate, and the timestamp of the video data at that time can be used to determine the timing at which the device disassembly process S01 and the maintenance process S02 are separated. Furthermore, image recognition can be used to capture the timing at which the worker's hand releases the O-ring 406 attached to the lower unit 403, and the timestamp of the video data at that time can be used to determine the timing at which the maintenance process S02 and the part assembly process S03 are separated. Other measurement data acquired simultaneously with the video data can also be separated into process units based on the timestamps. It is up to the user to decide how far to divide a process into a single unit, but for example, it is possible to divide a task that is grouped together in a work procedure manual into a single process. Next, the work is evaluated based on the measurement data divided into each process using the timestamp as the standard.

[0024] First, we will explain the evaluation of task complexity. In evaluating complexity, a process is further broken down into task elements, and the measurement data separated into each process is further broken down into measurement data for each task element, and then the evaluation is performed. This breakdown into task elements can also be done using the same method as the process separation described above.

[0025] Here, the work elements are predefined in the work ease evaluation table shown in Figure 6 as tasks at the finest granularity for evaluating the ease of work. In the work ease evaluation table, work elements are classified according to the ease of work and assigned an evaluation value. For example, moving a part from top to bottom is an easy task due to gravity, and is evaluated as A (easy). On the other hand, moving a part sideways or upward is more difficult, and is evaluated as B (average). The example work elements shown in Figure 6 are only a small portion, and the table has been created to be comprehensive so that all tasks involved in maintenance work on semiconductor manufacturing equipment fall into one of the work elements.

[0026] The complexity assessment of the equipment disassembly process S01 is shown in Figure 5A. Work element number 501 is a number that identifies a work element included in the process to be analyzed, and work element 502 indicates the content of the work element included in the process. Work time 503 is the work time required to perform the work element, and is measured from measurement data. If a work element is performed multiple times within a process, it shows, for example, the average time. Work ease 504 shows the assessment value assigned to the work element in the work ease assessment table. For example, A means easy, B means average, and C means difficult. Complexity assessment (by work element) 505 shows the complexity assessment score for each work element, and complexity assessment (process) 506 shows the complexity assessment score for the process as a whole.

[0027] The complexity evaluation score for each work element is calculated quantitatively using work time and work ease as indicators. Here, work time is divided into short, medium, and long, with evaluation scores of 0.5, 1, and 1.5, respectively, and work ease A, B, and C are assigned evaluation scores of 10, 20, and 30, respectively. The complexity evaluation score for each work element is calculated as the product of the evaluation score for work time and the evaluation score for work ease. In this case, if a work element is performed repeatedly (work elements No. 2 to 4), the result is further multiplied by the number of repetitions. The complexity evaluation score for the entire process is calculated as the sum of the complexity evaluation scores for each work element. Note that the calculation and allocation methods for evaluation scores shown here are just examples.

[0028] The complexity evaluation of the maintenance process S02, calculated in a similar manner, is shown in Figure 5B. In this example, the complexity evaluation score of the equipment disassembly process S01 was 355 points, and the complexity evaluation score of the maintenance process S02 was 40 points. Based on the complexity evaluation scores, the equipment disassembly process S01 can be evaluated as being more complex than the maintenance process S02.

[0029] As the number of samples taken for measurement data increases, the task time is averaged. Therefore, by increasing the number of samples, the accuracy of the task time gradually improves, and the accuracy of the complexity evaluation can be improved.

[0030] Next, we will explain how to evaluate task dexterity. Dexterity is evaluated using an index that changes depending on the worker's proficiency. For example, the variation in task time for task elements is thought to be an effective index. Figure 7 shows a histogram 701 of the task time for task element A and a histogram 702 of the task time for task element B. From histogram 701, it can be determined that task element A has little variation between workers and is not significantly affected by the worker's proficiency, experience, or knowledge. From histogram 702, it can be determined that task element B has large variation between workers and is significantly affected by the worker's proficiency, experience, and knowledge. The magnitude of the variation in task time can be quantitatively grasped, for example, by calculating the variance of the histogram.

[0031] In addition to task time variability, we also add motion variability and task success rate as indicators of dexterity assessment. Motion variability directly assesses the worker's motion or gaze movement. Experts tend to perform efficient and effective work. Therefore, for example, we can monitor the movements of experts to identify ideal motion and evaluate deviations from it. If most of the sampled measurement data closely resembles the ideal motion, we can assess motion variability as small. On the other hand, if the sampled measurement data contains many deviations from the ideal motion, we can assess motion variance as large. For example, we can compare the spatial relationship between the ideal motion trajectory and the measured motion trajectory of the worker's motion based on work video data measured with an RGBD camera and 3D distance measurement data to calculate the deviation rate from the ideal motion. Furthermore, we can evaluate deviations from the ideal motion by comparing the contact time with the part being worked on, the head direction and gaze direction obtained from the HMD worn by the worker, and other factors. The task success rate is calculated by determining the ratio of the number of successes to the number of executions of a task element, assuming that a task has failed if a problem is discovered in the task in a subsequent process and rework is required.

[0032] Figure 8A shows the dexterity evaluation for the equipment disassembly process S01. Task element number 801 is a number identifying a task element included in the process being analyzed, and task element 802 indicates the content of the task element included in the process. Time variance 803 is the variance of the task time required to perform the task element, measured from the measurement data. Here, it is shown as three categories (small, medium, large) rather than as a value itself. Movement variance 804 indicates the variance described above for each body element and gaze, etc. These can also be quantitatively calculated as variances from histograms, but here it is shown as three categories (small, medium, large) rather than as a value itself. In addition, the evaluation results of the element movement variances obtained from the measurement data are combined to determine the overall movement variance. Task success rate 805 indicates the task success rate described above. Dexterity evaluation (by task element) 806 indicates the dexterity evaluation score for each task element, and dexterity evaluation (process) 807 indicates the dexterity evaluation score for the entire process.

[0033] In this example, the dexterity evaluation score for each task element is calculated quantitatively using time variance, movement variability (overall), and task success rate as indicators. Here, time variance and movement variability are classified as small, medium, and large, and assigned scores of 1, 1.5, and 2, respectively. The dexterity evaluation score for each task element is calculated as the product of (100 - task success rate [%]), the time variance evaluation score, and the movement variability (overall) evaluation score. The dexterity evaluation score for the entire process is calculated as the average of the dexterity evaluation scores for each task element. Note that the calculation method and scoring method shown here are merely examples. Also, while the example shown here uses a dexterity evaluation on a task element basis, it is acceptable to use a single unit of action; for example, dexterity evaluation can be performed on multiple task elements performed consecutively as a single unit.

[0034] The task improvement approach determination unit 222 determines the direction in which the task in a process should be improved, using the complexity evaluation and dexterity evaluation performed for each process by the workability evaluation unit 221. Figure 9 shows the determination flow executed by the task improvement approach determination unit 222.

[0035] First, the workability evaluation results are obtained (S51). In the above example, the complexity evaluation result shown in Fig. 5A and the dexterity evaluation result shown in Fig. 8A are obtained for the equipment disassembly process S01, and the complexity evaluation result shown in Fig. 5B and the dexterity evaluation result shown in Fig. 8B are obtained for the maintenance process S02.

[0036] Next, the complexity evaluation result is compared with a predetermined threshold (S52). If the complexity evaluation score is equal to or greater than the threshold, "task simplification" is determined to be the work improvement approach (S53). For example, if the threshold is set to 250 points, the complexity evaluation score (355 points) of the equipment disassembly process S01 is equal to or greater than the threshold, and therefore the work improvement approach is determined to be "task simplification." On the other hand, the complexity evaluation score (40 points) of the maintenance process S02 is less than the threshold. A process determined to be "task simplification" can be said to be an overly complicated and redundant work process, so the first step is to simplify such a process.

[0037] If the complexity evaluation score is below the threshold, the dexterity evaluation result is compared with a predetermined threshold (S54). If the dexterity evaluation score is equal to or greater than the threshold, "advanced work support" is determined to be the work improvement approach (S55). For example, if the threshold is set at 40 points, the dexterity evaluation score (53.7 points) of the maintenance process S02 is equal to or greater than the threshold, and therefore the work improvement approach is determined to be "advanced work support." Processes determined to be "advanced work support" are low-complexity, simplified work processes, but are highly dexterous and difficult to automate using robots, etc. For this reason, it is effective to create mechanisms to compensate for lack of worker proficiency by enhancing work support, such as by using AR / VR.

[0038] If the dexterity evaluation score is below the threshold, "automation" is determined as the work improvement approach (S56). A process determined to be "automated" is low in complexity, simplified, and low in dexterity, so it would be effective to reduce human work by using robots or other tools.

[0039] The results of the work improvement approach determination for each process as described above are stored in the storage device 203 of the work analysis device 140 (S57). In this way, in this embodiment, by evaluating processes from the perspectives of complexity and dexterity, it is possible not only to improve the work efficiency of existing processes but also to optimize the work by reviewing the processes themselves. Furthermore, in addition to determining the work improvement approach, it is desirable to make improvement proposals in line with the determined approach.

[0040] First, a process determined to require "task simplification" is a process that has been evaluated as being excessively complicated and redundant. One improvement method is to replace work elements with high evaluation values ​​with work elements with low evaluation values, thereby lowering the process complexity evaluation value. Figure 10 shows an example of a work element conversion table. The work element conversion table is the work ease evaluation table shown in Figure 6, with information on work elements that are candidates for improvement of work elements with high evaluation values ​​added. In the example of Figure 10, the work elements that are candidates for improvement and their evaluation values ​​are added. The work improvement approach determination unit 222 uses the work element conversion table to create a process improvement proposal by replacing work elements with high evaluation values ​​included in a process determined to require "task simplification" with work elements with lower evaluation values, and calculates the complexity evaluation value that will be improved in this case and stores it together with the determination result.

[0041] Furthermore, for processes judged to be "automatable," it is considered effective to reduce the amount of human labor involved. However, because replacing processes with robots incurs costs for mechanization, it is preferable to evaluate the costs and then propose work improvement. Figure 11 shows the process automation proposal creation flow performed by the work improvement approach determination unit 222. First, the work elements included in the process are replaced with machine functions (S61). To do this, the machine function conversion table shown in Figure 12 is used. The machine function conversion table contains functions 1201 to be mechanized and costs 1202 required to mechanize the functions. In step S61, costs are calculated assuming that all mechanizable work elements included in the process are mechanized. Next, it is determined whether the calculated cost is within the user's acceptable range (S62). The user may set an upper limit on the cost they are willing to accept for automation in advance, or the calculated cost may be displayed on a GUI, prompting the user to accept or reject the proposal and, if not, to enter the upper limit. If the cost is within the acceptable range, the content created in step S61 is considered a process automation proposal (S64). On the other hand, if the cost is over budget, a process automation proposal is created in which some of the work elements that can be replaced with machine functions are replaced with machine functions so that the cost falls within the allowable cost range (S63, S64).

[0042] Note that the flow in Figure 9 is just one example, and various modifications are possible. For example, the "advanced work support" determination is made based on an evaluation of workability alone, but the "advanced work support" determination may also be made after a cost evaluation. Alternatively, the "work simplification" determination is made based on a complexity evaluation, but the user may also be allowed to select "equipment simplification." Equipment simplification is an approach that aims to simplify the process by changing the structure of the equipment or product (in this example, semiconductor manufacturing equipment) that is the target of the work, and if the user does not select "equipment simplification," the user may be allowed to select "work simplification."

[0043] The work improvement approach display generation unit 223 summarizes the analysis results for the work described above and displays them as a work analysis report via the analysis result output unit 225. Fig. 13 shows an example of a work analysis report display screen. A summary 1301 displays a summary of the analysis results. The improvement approach display unit 1302 displays a summary of the improvement approach determined for the work analyzed by the measurement data analysis unit 220. The number of improvement approaches, the specific target processes, costs, etc. are displayed for each improvement approach. The workability analysis report 1303 displays the analysis results of the workability that formed the basis for determining the analysis approach for the selected process. The example in Fig. 13 is an example in which a workability analysis report is displayed for process A, which was determined to be "work simplification." The user checks this content and makes improvements to the work.

[0044] Note that the display of some of the content may be modified or restricted depending on the position or role of the person viewing this screen. For example, the display shown in Fig. 13 is an example of a display presented to a user whose role is to promote work improvement, which is the original purpose. However, when presenting the display to a user whose role is to provide work training to workers or to the workers themselves, it is advisable to extract and present data for worker A, who is the training target, along with the overall distribution of the productivity analysis report 1303. This makes it possible to evaluate and confirm, for example, the overall level of dexterity of worker A. This type of display has the secondary effect of being useful from the perspective of work training for workers.

[0045] The above embodiments and modifications have been described in detail to make the present invention easier to understand, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment or modification with the configuration of another embodiment or modification, and it is also possible to add the configuration of one embodiment or modification to the configuration of another embodiment or modification. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment or modification with other configurations. [Explanation of symbols]

[0046] 100...Semiconductor manufacturing equipment, 101, 102...Camera, 103...HMD, 104...Work clothes sensor, 105...Glove-type sensor, 106...360° camera, 110...Measurement data collection device, 120...Measurement data storage unit, 130...Network, 140...Work analysis device, 201...Processor (CPU), 202...Memory, 203...Storage device, 204...Input interface, 205...Output interface, 206...Communication interface, 207...Bus, 208...Input device, 209...Display device, 210...Work measurement unit, 220...Measurement data analysis unit, 221...Workability evaluation unit, 222...Work improvement approach determination unit, 223...Work improvement approach table display generation unit, 225...analysis result output unit, 401...main body unit, 402...upper unit, 403...lower unit, 405, 406...O-ring, 501...task element number, 502...task element, 503...task time, 504...task ease, 505...complexity evaluation (by task element), 506...complexity evaluation (process), 701, 702...histogram, 801...task element number, 802...task element, 803...time variance, 804...motion variability, 805...task success rate, 806...dexterity evaluation (by task element), 807...dexterity evaluation (process), 1201...function, 1202...cost, 1301...summary, 1302...improvement approach display unit, 1303...workability analysis report.

Claims

1. a work measurement unit that collects measurement data from a sensor that measures the worker's movements during a predetermined work; a measurement data storage unit that stores the measurement data collected by the operation measurement unit; a measurement data analysis unit that analyzes the predetermined work based on the measurement data stored in the measurement data storage unit, the measurement data analysis unit includes a workability evaluation unit that evaluates, as workability of the predetermined work, complexity indicating the complexity of the predetermined work and dexterity indicating the degree of experience and knowledge required to perform the predetermined work, based on the measurement data accumulated in the measurement data accumulation unit; the workability evaluation unit performs a complexity evaluation based on the work time required for a work element constituting a worker's movement included in a process constituting the predetermined work and the work ease of the work element; The workability evaluation unit is a work analysis system that evaluates dexterity for each group of worker actions included in the process based on the variability in work time, action variability, and work success rate.

2. In claim 1, The measurement data analysis unit includes a task improvement approach determination unit that determines a task improvement approach for the specified task based on the task performance evaluated by the task performance evaluation unit.

3. In claim 2, The work improvement approach determination unit selects, as the work improvement approach, simplification of the work if the complexity of the specified work is determined to be high, or simplification of the specified equipment if the specified work is work that targets a specified device; selects advanced work support that enhances support for the worker if the complexity of the specified work is determined to be low and the dexterity is high; and selects automation that replaces the work performed by a worker with machine functions if the complexity and dexterity of the specified work are both determined to be low. This is a work analysis system.

4. In claim 1, The measurement data analysis unit is a work analysis system that divides the measurement data into multiple processes based on video data of the specified work taken by a camera, and analyzes the specified work for each process.

5. In claim 4, The workability evaluation unit breaks down the actions of the workers included in the process into work elements, performs a complexity evaluation for each work element based on the work time required for the work element and the ease of the work for the work element, and integrates the complexity evaluations for each work element included in the process to perform a complexity evaluation of the process.

6. In claim 4, The work performance evaluation unit performs a dexterity evaluation for each group of actions performed by the worker included in the process based on the variation in work time, variation in actions, and task success rate, and integrates the dexterity evaluations for each group of actions performed by the worker included in the process to perform a dexterity evaluation of the process.

7. In claim 3, a work analysis system in which, when task simplification is selected as the task improvement approach, the task improvement approach determination unit creates a task improvement proposal in which the task elements included in the specified task are replaced with task elements of less complexity.

8. In claim 3, a work analysis system in which, when automation is selected as the work improvement approach, the work improvement approach determination unit creates a work improvement proposal in which the work elements included in the specified work are replaced with machine functions;

9. In claim 8, When automation is selected as the work improvement approach, the work improvement approach determination unit creates a work improvement proposal in which some of the work elements included in the specified work are replaced with machine functions so as to satisfy a specified cost upper limit.

10. In claim 1, The predetermined work is a maintenance work for semiconductor manufacturing equipment.

11. The work measurement unit collects measurement data from a sensor that measures the worker's movements during a predetermined work task, the measurement data accumulation unit accumulates the measurement data collected by the work measurement unit; the measurement data analysis unit evaluates the workability of the predetermined work based on the measurement data stored in the measurement data storage unit, by evaluating complexity, which indicates the complexity of the predetermined work, and dexterity, which indicates the degree of experience and knowledge required to perform the predetermined work; the measurement data analysis unit performs a complexity evaluation based on the task time required for task elements constituting the worker's movements included in the steps constituting the predetermined task and the task ease of the task elements; The measurement data analysis unit performs a dexterity evaluation for each group of worker actions included in the process based on the variability in work time, the variability in actions, and the task success rate.

12. In claim 11, The measurement data analysis unit determines an approach to improving the predetermined task based on the workability of the predetermined task.

13. In claim 12, The measurement data analysis unit selects, as the work improvement approach, simplification of the work if the complexity of the specified work is determined to be high, or simplification of the specified equipment if the specified work is work that targets a specified device; selects advanced work support that enhances support for the worker if the complexity of the specified work is determined to be low and the dexterity is high; and selects automation that replaces the work of the worker with machine functions if the complexity and dexterity of the specified work are both determined to be low.

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