Job analysis system and job analysis method

Abstract

A work analysis system is provided with: a work measurement unit (210) that collects measurement data from a sensor that measures the operation of a worker in a given 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 a prescribed job on the basis of the measurement data accumulated in the measurement data accumulation unit, the measurement data analysis unit being provided with: an operability evaluation unit (221) that evaluates the operability of the prescribed job on the basis of the measurement data accumulated in the measurement data accumulation unit; and a work improvement scheme determination unit (222) that determines a work improvement scheme for the given work on the basis of the workability of the given work evaluated by the workability evaluation unit.

Description

Technical Field

[0001] The present invention relates to a job analysis system and a job analysis method. Background Art

[0002] In order to improve the productivity of semiconductor devices, semiconductor manufacturing equipment is required not only to improve its performance, but also to improve the operating rate through efficient maintenance. The maintenance of semiconductor manufacturing equipment mostly involves manual work. In the face of insufficient manpower and insufficient skilled personnel, efficient maintenance is an important issue.

[0003] Patent document 1 discloses a learning support system that can efficiently master a technique. The learning support system includes: a display unit provided for a learner; a camera unit provided for the learner to capture a visual field image of the learner; and a storage unit that stores a demonstration dynamic image, which is a dynamic image of an instructor's work action as a demonstration of the learner's action, so that the demonstration dynamic image is displayed on the display unit in an overlapping manner with the visual field image captured by the camera unit, and the display content of the demonstration dynamic image is dynamically changed according to the characteristics of the learner's work action contained in the visual field image.

[0004] Prior Art Literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2020-144233 Summary of the invention

[0007] -Problems to be solved by the invention-

[0008] As shown in Patent Document 1, many studies have been conducted on the idea of ​​using sensors to monitor and support manual work. These ideas are expected to improve the efficiency of manual work, but on the other hand, the work is performed manually, and improvements cannot be made beyond the limits of human capabilities.

[0009] In order to cope with the shortage of people and skilled workers that will further develop in the future, it is indispensable to reconsider the current operation or the structure of the device (product) for which the operation is set as the object from the perspective of whether the operation is necessary or whether it is appropriate to perform the operation manually, and seek overall optimization. The purpose of the present invention is to provide a job analysis system and a job analysis method that provide a perspective for improving the operation based on the monitoring data of the operation of the sensor.

[0010] -Methods for solving the problem-

[0011] A job analysis system according to one embodiment of the present invention comprises: a job measurement unit that collects measurement data from a sensor that measures the movements of a worker in a given job; a measurement data storage unit that stores the measurement data collected by the job measurement unit; and a measurement data analysis unit that analyzes a given job based on the measurement data stored in the measurement data storage unit, the measurement data analysis unit comprising: an operability evaluation unit that evaluates the operability of a given job based on the measurement data stored in the measurement data storage unit; and a job improvement plan determination unit that determines a job improvement plan for a given job based on the operability of the given job evaluated by the operability evaluation unit.

[0012] -Effects of the Invention-

[0013] The present invention provides a work analysis system and a work analysis method that provide a viewpoint for work improvement based on work monitoring data from a sensor. Other problems and novel features will become apparent from the description of this specification and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a summary structure diagram of the job parsing system.

[0015] Figure 2A It is the hardware structure of an information processing device.

[0016] Figure 2B This is a functional block diagram of the job analysis system.

[0017] Figure 3 This is a flowchart of maintenance work for semiconductor manufacturing equipment.

[0018] Figure 4 The diagram is for explaining the work performed in each process of the maintenance work.

[0019] Figure 5A This is an example of evaluating the complexity of the device dismantling process.

[0020] Figure 5B This is an example of evaluating the complexity of a maintenance process.

[0021] Figure 6 This is an example of a task ease evaluation form.

[0022] Figure 7 It is a histogram of job time.

[0023] Fig. 8A This is an example of evaluating the precision of the device disassembly process.

[0024] Figure 8B This is an example of evaluating the sophistication of a maintenance process.

[0025] Fig. 9It is a flow chart for determining the work improvement plan.

[0026] Fig.10 This is an example of a work element transformation table.

[0027] Fig.11 This is a flowchart for proposing the automation of the process.

[0028] Fig.12 is an example of a mechanical function conversion table.

[0029] Fig.13 This is an example of the job analysis report display screen. DETAILED DESCRIPTION

[0030] Figure 1 A summary structure diagram showing a work analysis system. Here, the maintenance work performed by the operator on the semiconductor manufacturing device 100 is taken as an example to explain the process of monitoring the operator's work and analyzing the work and making improvement proposals through the work analysis system of this embodiment. The work analysis system has: a sensor group 101 to 106 that monitors the operator's work on the semiconductor manufacturing device 100; a measurement data collection device 110 that collects measurement data related to the operator's actions during the work detected by the sensor group 101 to 106; a measurement data storage unit 120 that stores the measurement data collected by the measurement data collection device 110; and a work analysis device 140 that analyzes the work and makes improvement proposals based on the measurement data stored in the 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 through a network 130 so as to be able to communicate with each other. The network 130 can be wired or wireless, and the communication standard is also arbitrary.

[0031] Figure 2A The hardware configuration of the measurement data collection device 110 and the work analysis device 140 is shown. Figure 2AThe information processing device shown in the figure is implemented as the main structure of the processor (CPU) 201, memory 202, storage device 203, input interface (I / F) 204, output I / F 205, communication I / F 206, and bus 207. The processor 201 performs functions as a functional unit (functional block) that provides a given function by executing processing according to the program loaded into the memory 202. The storage device 203 stores data and programs used in the functional unit. The storage device 203 uses a non-volatile storage medium such as HDD (Hard Disk Drive) and SSD (Solid State Drive). The input I / F 204 is an interface for connecting an input device 208 such as a keyboard and a pointing device, and the output I / F 205 is an interface for connecting a display device 209. The communication I / F 206 can communicate with other information processing devices via the network 130. These are connected through the bus 207 so that they can communicate with each other.

[0032] In addition, the measurement data collection device 110 and the operation analysis device 140 do not need to be implemented by separate information processing devices, but can be implemented on one information processing device, and the measurement data can be stored in the storage device 203. In this case, the storage device 203 functions as the measurement data storage unit 120. In addition, part or all of the functions of the measurement data collection device 110 and the operation analysis device 140 can also be implemented as applications on the cloud.

[0033] Figure 2B The work measurement unit 210 of the measurement data collection device 110 controls the sensor group and accumulates the measurement data in the measurement data accumulation unit 120. Figure 1 As shown, the sensor group includes a camera 101 for taking a bird's-eye view of the operator's work, a device-mounted camera 102, a HM (Head Mounted Display) 103, a work clothes sensor 104 worn by the operator, a glove-type sensor 105, and a 360° camera 106 for taking a picture of the entire work area. However, Figure 1The sensor group shown is an example, and sensors other than the examples may be used or not. The work measurement unit 210 uses these sensors to monitor the status of the worker's work. Thus, for example, RGB (color) data (dynamic image data) is stored in the measurement data storage unit 120 as measurement data from the camera. When a sensor that can obtain the distance to the object in addition to RGB data, i.e., an RGBD camera, is used as the camera, in addition to the dynamic image data, distance measurement data representing the distance information to the object is stored in the measurement data storage unit 120 as measurement data, and the worker's line of sight activity data is stored in the measurement data storage unit 120 from the HM 103 as measurement data, the worker's bone activity data is stored in the measurement data storage unit 120 from the work clothes sensor 104 as measurement data, and the worker's finger activity data is stored in the measurement data storage unit 120 from the glove sensor 105 as measurement data. It is desirable to record all the measurement data accumulated in the measurement data accumulation unit 120 with a time stamp (time information) based on the same reference time. This allows the work analysis device 140 to integrate measurement data from multiple sensors and analyze the work.

[0034] 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 result of the measurement data analysis unit 220 is displayed on the display device 209 of the work analysis device 140 through the analysis result output unit 225. The processing of the measurement data analysis unit 220 will be described later with reference to a specific example.

[0035] First, use Figure 3 as well as Figure 4 The maintenance work of a semiconductor manufacturing device is described as a specific example. The maintenance work is an example of a work in which the semiconductor manufacturing device is disassembled, the target unit is maintained, and then the components are assembled to return to a state where it can be operated, such as Figure 3 As shown, it is generally composed of three steps: device disassembly (S01), maintenance of the target unit (S02), and component assembly (S03). In addition, this specific example is given for the purpose of specifically explaining the processing in this embodiment, and the analysis object of the operation analysis system and operation analysis method of this embodiment is not limited to this example.

[0036] use Figure 4 Describe the work performed in each process. Figure 4, the semiconductor manufacturing apparatus is schematically shown to include a main body 401, an upper unit 402, and a lower unit 403. For example, when the semiconductor manufacturing apparatus is a plasma processing apparatus, the upper unit 402 is a chamber for generating plasma, and the lower unit 403 is a vacuum container for placing a sample to be processed. The maintenance part in this specific example is the lower unit 403.

[0037] The device disassembly process (S01) corresponds to states S11 to S14. State S11 is the state at the start of the operation, and state S12 is the state in which the upper unit 402 and the lower unit 403 connected to each other are separated from the main body 401. State S13 represents the state in which the screws are manually removed using a tool such as a screwdriver in order to separate the upper unit 402 from the lower unit 403. State S14 is the state in which the upper unit 402 is separated from the lower unit 403 and the lower unit 403 can be operated.

[0038] The maintenance process (S02) corresponds to states S21 to S23. State S21 indicates that the O-ring 405, which is a consumable part, is removed from the lower unit 403. State S22 indicates that the lower unit 403 is cleaned by wiping away the attachments and dirt. State S23 indicates that the consumable part is replaced and a new O-ring 406 is installed.

[0039] The component assembly process (S03) corresponds to state S31 to state S33. In state S31, the separated upper unit 402 and lower unit 403 are matched, and screws are tightened to connect them in state S32. By assembling the upper unit 402 and lower unit 403 connected in state S33 to the main body 401, the assembly work is completed.

[0040] Taking the maintenance work described above as an example, the analysis of the measurement data performed by the measurement data analysis unit 220 will be described. The measurement data analysis unit 220 includes a workability evaluation unit 221, a work improvement plan determination unit 222, and a work improvement plan display generation unit 223 (see Figure 2B ) First, the workability evaluation unit 221 evaluates the workability using the measurement data.

[0041] The workability evaluation unit 221 evaluates the operation from the perspective of complexity and sophistication. In order to ensure the objectivity of the evaluation, the collected measurement data is used to quantitatively calculate the evaluation value according to the predetermined index during the evaluation. Here, the complexity of the operation refers to the complexity of the operation completed by the operator. For example, the number of operation elements constituting the operation, the ease of the operation indicating the ease of the operation elements, the length of the operation time, etc. become indicators. On the other hand, the sophistication of the operation means the experience value and the degree of knowledge required by the operator to complete the operation. Specifically, the operation in which there is almost no difference in the operation efficiency and operation quality between the skilled and the unskilled is evaluated as a low-skilled operation, and the operation in which there is a large difference in the operation efficiency or operation quality between the skilled and the unskilled is evaluated as a high-skilled operation. For example, the deviation (variance) of the operation time depending on the operator, the deviation of the movement of the skilled and the unskilled during the operation, the content rate of the operation elements evaluated as high in sophistication, the success rate of the operation, etc. become indicators.

[0042] In the evaluation of the complexity and sophistication of work, it is desirable to monitor the work of a large number of workers for the work to be analyzed many times and accumulate measurement data. This is because the more measurement data is accumulated, the more statistically accurate and reliable the evaluation can be.

[0043] First, the operability evaluation unit 221 performs pre-processing to divide the measurement data into processes y. For example, in this example, the measurement data is divided into three processes: the device disassembly process S01, the maintenance process S02, and the component assembly process S03. The division can perform image analysis on the dynamic image data that captures the status of the operation, identify characteristic objects and operations, and determine the timing of the division of the process. For example, the timing of the separation of the upper unit 402 and the lower unit 403 can be captured by image recognition, and the timestamp of the dynamic image data at this time is set as the division timing of the device disassembly process S01 and the maintenance process S02. In addition, the timing of the operator's hand leaving the O-ring 406 installed on the lower unit 403 can be captured by image recognition, and the timestamp of the dynamic image data at this time is set as the division timing of the maintenance process S02 and the component assembly process S03. For other measurement data obtained simultaneously with the dynamic image data, it is also possible to divide it into units of processes based on the timestamp. In addition, it is arbitrary to what extent a series of processes are divided, and for example, it is considered that a series of operations in the operation procedure book are divided into one process. Then, the operation is evaluated based on the measurement data divided for each process with the time stamp as the reference.

[0044] First, the complexity evaluation of the operation is described. When performing the complexity evaluation, the process is further decomposed into operation elements, and the measurement data divided for each process is further decomposed into the measurement data of each operation element for evaluation. In addition, the decomposition into operation elements can also be performed by the same method as the division of the process described above.

[0045] Here, the work element is the final granularity of work for evaluating the ease of work. Figure 6 The work ease evaluation table shown in the figure is predefined. In the work ease evaluation table, work elements are distinguished according to the ease of work and evaluation values ​​are assigned. For example, the downward movement of a component from top to bottom is an action that is easy to work due to gravity, and is evaluated as A (easy). On the other hand, lateral movement and upward movement are actions that are more difficult to work, and are evaluated as B (normal). Figure 6 The illustrated work elements are only a few, and are created in an all-inclusive manner so that the work included in the maintenance work for the semiconductor manufacturing equipment corresponds to any of the work elements.

[0046] Figure 5A Indicates the complexity evaluation of the device disassembly process S01. The work element number 501 is the number that determines the work element included in the process set as the analysis object, and the work element 502 indicates the content of the work element included in the process. The work time 503 is the work time required to execute the work element, which is measured based on the measurement data. In the case where the work element is executed multiple times in the process, for example, it indicates the average time. The work ease 504 indicates the evaluation value assigned to the work element in the work ease evaluation table. For example, A means easy, B means normal, and C means difficult. Complexity evaluation (by work element) 505 indicates the complexity evaluation score by work element, and complexity evaluation (process) 506 indicates the complexity evaluation score as a whole process.

[0047] The complexity evaluation score for each work element is quantitatively calculated using work time and work ease as indicators. Here, the work time is divided into short, medium, and long, and the evaluation scores are set to 0.5, 1, and 1.5 respectively. The evaluation scores of A, B, and C for work ease are set to 10, 20, and 30 respectively. The complexity evaluation score for each work element is calculated as the product of the evaluation score for the work time and the evaluation score for the work ease. At this time, when the work element is repeatedly implemented (work element No. 2 to 4), it is further multiplied by the number of repetitions. The complexity evaluation score of the entire process is calculated as the sum of the complexity evaluation scores for each work element. In addition, the calculation method and scoring method of the evaluation score shown here are just an example.

[0048] Figure 5BThe complexity evaluation of the maintenance process S02 obtained in the same manner is shown. In this example, the complexity evaluation score of the device dismantling process S01 is 355 points, and the complexity evaluation score of the maintenance process S02 is 40 points. Based on the complexity evaluation score, the device dismantling process S01 can be evaluated as a more complex process than the maintenance process S02.

[0049] If the number of samples for obtaining measurement data increases, the operation time is averaged. Therefore, by increasing the number of samples, the accuracy of the operation time gradually increases, and the accuracy of the complexity evaluation can be improved.

[0050] Next, the evaluation of the workmanship is described. The workmanship is evaluated by an index that changes according to the worker's proficiency. For example, the variation in the work time of the work elements is considered to be an effective index. Figure 7 A histogram 701 showing the operation time of operation element A and a histogram 702 showing the operation time of operation element B. According to the histogram 701, operation element A can be judged as an operation with little variation among operators and little influence of the operator's proficiency, experience, and knowledge, and according to the histogram 702, operation element B can be judged as an operation with great variation among operators and strongly influenced by the operator's proficiency, experience, and knowledge. The magnitude of the variation in operation time can be quantitatively grasped by calculating the variance of the histogram, for example.

[0051] Here, in addition to the deviation of the operation time, the action deviation and the operation success rate are also added as indicators for evaluating the precision. The action deviation is obtained by directly evaluating the action of the operator or the movement of the line of sight, etc. In the case of a skilled person, the operation action becomes an effective action without waste. Therefore, for example, it is considered to monitor the activities of a skilled person to determine the ideal operation action and evaluate the deviation from this. In the case where most of the sampled measurement data show an approximation to the ideal operation action, it can be evaluated that the action deviation is small, and in the case where the sampled measurement data contains more measurement data that deviates from the ideal operation action, it can be evaluated that the action deviation is large. For example, based on the operation image data measured by the RGBD camera and the three-dimensional ranging data, the spatial positional relationship between the trajectory of the ideal operation action and the measured trajectory of the operator's operation action is compared to calculate the separation rate from the ideal operation action, or the contact time with the component as the operation object, the direction of the head in the operation that can be obtained from the HM equipped by the operator, the gaze destination of the line of sight, etc., are compared with the ideal operation action, thereby being able to evaluate the deviation from the ideal operation action. The work success rate is obtained by determining the ratio of the number of successes to the number of executions of the work element, assuming that a problem is found in the work in the subsequent process and rework is required as a failure.

[0052] Fig. 8AIndicates the precision evaluation of the device disassembly process S01. The work element number 801 is the number that determines the work element included in the process set as the analysis object, and the work element 802 indicates the content of the work element included in the process. The time variance 803 is the deviation of the work time required to execute the work element, and is the variance of the work time measured based on the measurement data. Here, the value itself is not shown, for example, it is expressed as three distinctions of small, medium and large. The action deviation 804 indicates the deviation as described above for each element of the body, line of sight, etc. This can also be quantitatively calculated as a variance based on the histogram, but it is not the value itself here, for example, it is expressed as three distinctions of small, medium and large. In addition, the comprehensive action deviation is obtained by synthesizing the evaluation results of the element action deviation obtained based on the measurement data. The operation success rate 805 indicates the above-mentioned operation success rate. The precision evaluation (differentiated by work element) 806 indicates the precision evaluation score differentiated by work element, and the precision evaluation (process) 807 indicates the precision evaluation score as the process as a whole.

[0053] In this example, the precision evaluation score for each operating element is quantitatively calculated using time variance, motion deviation (comprehensive) and operation success rate as indicators. Here, the time variance and motion deviation are divided into small, medium and large and are set to evaluation scores of 1, 1.5 and 2 respectively. The precision evaluation score for each operating element is calculated as the product of (100-operation success rate [%]), the evaluation score of the time variance and the evaluation score of the motion deviation (comprehensive). The precision evaluation score for the entire process is calculated as the average value of the precision evaluation scores for each operating element. In addition, the calculation method and scoring method of the evaluation score shown here are just examples. In addition, here, an example of evaluating precision based on operating elements is also shown, but it can be any series of action units, for example, multiple operating elements that are executed continuously can be evaluated for precision as a unit.

[0054] The work improvement plan determination unit 222 uses the complexity evaluation and the ingenuity evaluation performed for each process by the workability evaluation unit 221 to determine in which direction the work in the process is desired to be improved. Fig. 9 A flowchart showing the determination process executed by the work improvement plan determination unit 222 is shown.

[0055] First, the workability evaluation result is obtained (S51). In the above example, regarding the device disassembly step S01, the Figure 5A The complexity evaluation results shown and Fig. 8A The precision evaluation results shown in the figure are as follows: Figure 5B The complexity evaluation results shown and Figure 8B The results of the refinement evaluation are shown.

[0056] Next, the complexity evaluation result is compared with a predetermined threshold value (S52). If the complexity evaluation score is above the threshold value, "operation simplification" is determined as the operation improvement plan (S53). For example, if the threshold value is set to 250 points, the complexity evaluation score (355 points) of the device disassembly process S01 is above the threshold value, so it is determined that the operation improvement plan is "operation simplification". On the other hand, the complexity evaluation score (40 points) of the maintenance process S02 is less than the threshold value. Since the process determined as "operation simplification" is an overly complicated and lengthy operation process, such a process simplification is first implemented.

[0057] If the complexity evaluation score is less than the threshold, the precision evaluation result is compared with a predetermined threshold (S54). If the precision evaluation score is above the threshold, "higher level of operation support" is determined as the operation improvement plan (S55). For example, if the threshold is set to 40 points, the precision evaluation score (53.7 points) of the maintenance process S02 is above the threshold, so it is determined that the operation improvement plan is "higher level of operation support". The process determined as "higher level of operation support" is a simplified operation process with low complexity. On the other hand, it is difficult to automate processes with high precision and the use of robots. Therefore, it is effective to make components that make up for the lack of proficiency of operators by improving the level of operation support such as the use of AR / VR.

[0058] If the precision evaluation score is less than the threshold, "automation" is determined as a work improvement plan (S56). The process determined as "automation" is simple and has low complexity and low precision, so robots are effectively used to reduce human work.

[0059] The above-mentioned results of the determination of the work improvement plan for each process are accumulated in the storage device 203 of the work analysis device 140 (S57). In this way, in this embodiment, by evaluating the process from the perspective of complexity and sophistication, it is possible not only to improve the work efficiency of the existing process, but also to optimize the work by promoting the re-study of the process itself. Furthermore, in addition to determining the work improvement plan, it is preferred to also make improvement proposals in accordance with the determined plan.

[0060] First, a process determined as "operation simplification" is a process that is evaluated as excessively complicated and lengthy. As an improvement method, the complexity evaluation value of a process can be reduced by replacing a high-evaluation operation element with a low-evaluation operation element. Fig.10 This shows an example of a work element conversion table. Figure 6 The work ease evaluation table shown has information on work elements that are candidates for improvement for work elements with high evaluation values ​​added. Fig.10In the example of , the work elements set as improvement candidates and the evaluation values ​​for the improvement candidates are attached. The work improvement plan determination unit 222 uses the work element conversion table to replace the work elements with high evaluation values ​​included in the process determined as "work simplification" with work elements with lower evaluation values, thereby making a process improvement proposal, and calculates the complexity evaluation value of the improvement in this case, and stores it together with the determination result.

[0061] Furthermore, among the processes determined to be "automated", it is evaluated that it is effective to reduce the work of people, but the cost for mechanization is incurred in the replacement with robots, so it is preferable to make work improvement proposals based on cost evaluation. Fig.11 The flowchart of the process automation proposal preparation performed by the work improvement plan determination unit 222 is shown. First, the work elements included in the process are replaced with mechanical functions (S61). For this purpose, the work elements included in the process are replaced with mechanical functions (S62). Fig.12 The mechanical function conversion table shown. The mechanical function conversion table contains registered functions 1201 to be mechanized and costs 1202 required for the mechanization of the function. In step S61, it is assumed that all the work elements that can be mechanized included in the process are mechanized to calculate the cost. Next, it is determined whether the calculated cost is within the user's permitted range (S62). The user can either pre-set the upper limit of the cost allowed for automation, or display the calculated cost in the GUI and ask the user to input whether or not to agree, and if not, ask the user to input the upper limit of the cost. When the cost converges to the permitted range, the content produced in step S61 is used as a process automation proposal (S64). On the other hand, when the cost is high, a process automation proposal is produced to replace part of the work elements that can be replaced by mechanical functions with mechanical functions so that it converges to the permitted cost (S63, S64).

[0062] in addition, Fig. 9 The flowchart is an example and can be modified in various ways. For example, the "higher level of operation support" is determined only by the evaluation of operability, but the "higher level of operation support" can also be determined based on the cost evaluation. Alternatively, the "operation simplification" is determined based on the complexity evaluation, but the user can also be allowed to select "device simplification". Device simplification refers to a scheme to simplify the process by changing the structure of the device or product (in this case, the semiconductor manufacturing device) that is the object of the operation. Even if the user does not select "device simplification", "operation simplification" can be selected.

[0063] The work improvement plan display generation unit 223 collects the analysis results for the work described above and displays them as a work analysis report through the analysis result output unit 225 . Fig.13This shows an example of a job analysis report display screen. The summary 1301 shows a summary of the analysis results. The improvement plan display section 1302 shows a summary of the improvement plans determined for the job analyzed by the measurement data analysis section 220. The number of improvement plans and the specific target processes, costs, etc. are displayed for each improvement plan. The workability analysis report 1303 shows the workability analysis results that are the basis for determining the analysis plan for the selected process. Fig.13 In the example of , an operation performance analysis report is displayed for process A determined as "operation simplification". The user confirms the content and improves the operation.

[0064] In addition, it is also possible to perform control such as deforming or limiting the display of part of the content according to the position, job title, etc. of the person viewing the screen. Fig.13 The display shown is an example of a prompt to a user whose job is to promote work improvement, which is the original purpose. However, when the prompt is to a user whose job is to provide work education to a worker, or to the worker himself, the data of worker A, who is the education target, can be presented together with the overall distribution of the workability analysis report 1303. Thus, for example, the overall level of skill of worker A can be evaluated and confirmed. By viewing in this way, a secondary effect that can be utilized from the perspective of work education for workers can be obtained.

[0065] The above embodiments and variants are described in detail for easy understanding of the present invention, and are not necessarily limited to having all the structures described. In addition, a part of the structure of a certain embodiment or variant can be replaced with the structure of another embodiment or variant, and the structure of another embodiment or variant can be added to the structure of a certain embodiment or variant. In addition, with respect to a part of the structure of each embodiment or variant, other structures can be added / deleted / replaced.

[0066] -Description of Reference Numerals-

[0067] 100…semiconductor manufacturing device, 101, 102…camera, 103…HMD, 104…work clothes sensor, 105…glove 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 plan determination unit, 223…work improvement plan display generation unit, 225…analysis Result output section, 401…main body, 402…upper unit, 403…lower unit, 405, 406…O-ring, 501…operation element number, 502…operation element, 503…operation time, 504…operation ease, 505…complexity evaluation (by operation element), 506…complexity evaluation (process), 701, 702…histogram, 801…operation element number, 802…operation element, 803…time variance, 804…motion deviation, 805…operation success rate, 806…precision evaluation (by operation element), 807…precision evaluation (process), 1201…function, 1202…cost, 1301…summary, 1302…improvement plan display section, 1303…operability analysis report

Claims

1. A job analysis system, characterized in that: have: a work measurement unit that collects measurement data from sensors that measure the movements of a worker in a given work; a measurement data storage unit for storing the measurement data collected by the operation measurement unit; as well as a measurement data analyzing unit that analyzes the given operation based on the measurement data accumulated in the measurement data accumulating unit, The measurement data analysis unit comprises: a workability evaluation unit that evaluates workability of the given work based on the measurement data accumulated in the measurement data accumulation unit; and The work improvement plan determination unit determines a work improvement plan for the given work based on the workability of the given work evaluated by the workability evaluation unit.

2. The job analysis system according to claim 1, wherein: The workability evaluation unit evaluates complexity indicating the complexity of the given work and sophistication indicating the degree of experience and knowledge required to complete the given work as the workability of the given work.

3. The job analysis system according to claim 2, wherein: When the complexity of the given job is determined to be high, the job improvement plan determination unit selects simplification of the job as the job improvement plan, or, when the given job is a job with a given device as the object, selects simplification of the given device as the job improvement plan. When the complexity of the given job is determined to be low and the sophistication is high, selects improving the level of job support for the operator as the job improvement plan. When the complexity and sophistication of the given job are determined to be low, selects replacing the operator's job with automation of mechanical functions as the job improvement plan.

4. The job analysis system according to claim 2, wherein: The measurement data analysis unit divides the measurement data into a plurality of steps based on moving image data obtained by photographing the status of the given work with a camera, and analyzes the given work for each of the steps.

5. The job analysis system according to claim 4, wherein: The workability evaluation unit decomposes the actions of the operator included in the process into work elements, and performs complexity evaluation on each work element based on the work time required for the work element and the ease of operation of the work element, and integrates the complexity evaluation of each work element included in the process to perform complexity evaluation of the process.

6. The job analysis system according to claim 4, wherein: The workability evaluation unit performs precision evaluation on each series of actions of the operator included in the process based on the deviation of operation time, movement deviation and operation success rate, and integrates the precision evaluation of each series of actions of the operator included in the process to perform precision evaluation of the process.

7. The job analysis system according to claim 3, wherein: The work improvement plan determination unit generates a work improvement proposal for replacing work elements included in the given work with work elements of lower complexity when simplification of the work is selected as the work improvement plan.

8. The job analysis system according to claim 3, wherein: The work improvement plan determination unit generates a work improvement proposal for replacing work elements included in the given work with machine functions when automation is selected as the work improvement plan.

9. The job analysis system according to claim 8, wherein: The work improvement plan determination unit generates a work improvement proposal for replacing a part of work elements included in the given work with mechanical functions so as to satisfy a designated cost upper limit when automation is selected as the work improvement plan.

10. The job analysis system according to claim 1, wherein: The given task is a maintenance task of a semiconductor manufacturing device.

11. A method for analyzing a job, comprising: using a job analysis system; The work analysis system includes a work measurement unit, a measurement data storage unit, and a measurement data analysis unit including a measurement data analysis unit. The work measurement unit collects measurement data from a sensor that measures the movements of a worker in a given work. The measurement data storage unit stores the measurement data collected by the operation measurement unit. The measurement data analysis unit evaluates workability of the given task based on the measurement data accumulated in the measurement data accumulation unit, and determines a work improvement plan for the given task based on the workability of the given task.

12. The job analysis method according to claim 11, wherein: The measurement data analysis unit evaluates complexity indicating the complexity of the given task and sophistication indicating the degree of experience and knowledge required to complete the given task as workability of the given task.

13. The job analysis method according to claim 12, wherein: When the measurement data analysis unit determines that the given operation has high complexity, the measurement data analysis unit selects simplification of the operation as the operation improvement plan, or, when the given operation is an operation with a given device as the object, the measurement data analysis unit selects simplification of the given device as the operation improvement plan. When the measurement data analysis unit determines that the given operation has low complexity and high sophistication, the measurement data analysis unit selects improving the level of operation support for the operator as the operation improvement plan. When the measurement data analysis unit determines that both the complexity and sophistication of the given operation are low, the measurement data analysis unit selects replacing the operator's operation with automation of mechanical functions as the operation improvement plan.

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