JP7812288B2 - Work performance analysis system and work performance analysis method - Google Patents

Description

The present invention relates to a work performance analysis system and a work performance analysis method that estimate the behavioral history and factors of workers.

Maintenance work on large-scale facilities and equipment, such as regular inspections of power plants, can take many man-hours. However, not all of these maintenance tasks have an accurate understanding of the man-hours required, and many of them are not planned efficiently. Therefore, if efficient work plans can be created, the maintenance period for facilities and equipment can be shortened, and service outage periods can be reduced.

Maintenance work on large-scale facilities and equipment often involves manual labor, as many tasks are not automated. Therefore, understanding work hours means understanding the amount of time workers spend on tasks. In addition, to identify areas for improvement in work plans, it is also necessary to understand the proportion of time spent on non-work activities such as meetings, travel, and waiting, as well as the causes of waiting, and analyze these to increase the time available for work.

For example, Patent Document 1 describes a work management system that uses information collected by IC tags for identifying work to clearly display work processes divided into multiple work steps, allowing managers and others to easily grasp waste and problems that occur at the work site. The method described in Patent Document 1 makes it easy to see the progress of work using a diagram, making it easy to check differences between plans and actual results, and whether there are any delays due to waiting for workers or machines.

JP 2010-40007 A

The method in Patent Document 1 analyzes data on actual work performance, so it can show that the start or end of work was delayed compared to the plan, but it cannot identify the cause of the work delay or provide a breakdown of the time spent on non-work activities. Furthermore, in highly automated manufacturing lines, the operational status of equipment can be monitored and the monitoring results analyzed to determine the difference between the plan and the actual results. However, in cases where the work is performed by workers, such as in periodic inspections at power plants, analysis must be based on the worker's behavioral history. In particular, in the case of workers, important analysis items include the time spent on non-work activities such as meetings, travel, and waiting, as well as the location of the worker within the facility.

In light of the above, the present invention aims to provide a work performance analysis system and a work performance analysis method that can analyze a worker's behavioral history in detail using a small number of measurement items and measurement frequencies.

In light of the above, the present invention is a work performance analysis system that has a work location database that stores information indicating whether each location is a work location or not, and a work performance database that stores information on where a worker is staying at each time and the amount of time the worker has worked, and that uses the information in the work location database and the work performance database to classify the worker into four cases by combining two cases: when the worker is at a designated work location or not, and two cases: when the worker is working or not working, and tallying up the time required by the worker for each category, and estimating the worker's behavioral history.

Furthermore, the present invention provides a "work performance analysis method characterized by using a computer that utilizes a work location database that stores information indicating whether each location is a work location or not, and a work performance database that stores information on the location of workers at each time and the amount of time they spend working, to classify into four cases by combining two cases: when the worker is at a designated work location or not, and two cases: when the worker is working or not working, and by tallying up the time required by the worker for each classification, and estimating the worker's behavioral history."

This invention allows for detailed analysis of a worker's behavioral history using a small number of measurement items and measurement frequency.

1 is a diagram showing an example of the configuration of a work performance analysis system according to a first embodiment of the present invention; FIG. 10 is a diagram showing an example of worker location information D11 recorded in the work record database DB1. FIG. 10 is a diagram showing an example of work time information D12 recorded in the work performance database DB1. FIG. 4 is a diagram showing an example of information recorded in a work location database DB2. FIG. 10 is a diagram showing the processing flow of the accumulated data processing stage in the work performance analysis unit 13. FIG. 3 is a diagram showing location information D11 of FIG. 2 in chronological order. FIG. 10 is a diagram showing location information D11 in chronological order, reflecting work location data D2. A graph showing only net working hours over time. A diagram showing examples of classification results in chronological order. FIG. 10 is a diagram showing the processing flow of the work performance analysis stage in the work performance analysis unit 13. FIG. 10 is a diagram showing an example of a classification pattern. FIG. 10 is a diagram showing an example of a method for displaying the analysis results of the behavioral performance of a worker as a graph. FIG. 10 is a diagram showing an example of the configuration of a work performance analysis system 1 according to a second embodiment of the present invention. FIG. 4 is a diagram showing an example of information recorded in a non-work time database DB4. FIG. 10 is a diagram showing an example of a flow for selecting candidate actions in a non-work place. FIG. 10 is a diagram showing an example of a method for displaying a breakdown of non-work time as a graph. FIG. 10 is a diagram showing an example of the configuration of a work performance analysis system 1 according to a third embodiment of the present invention. 11A and 11B are flowcharts showing a series of analytical processing examples in the third embodiment. FIG. 4 is a diagram showing an example of information recorded in a work start condition database DB5. FIG. 10 is a diagram showing an example of a flow for selecting candidates for non-work activities at a work location. FIG. 10 is a diagram showing an example of displaying a breakdown of non-work activities at a work site as a graph. A graph showing the results reflecting all analytical methods. FIG. 10 is a diagram showing an example of displaying detailed analysis results of behavioral performance as a timing chart.

The following describes embodiments of the present invention with reference to the drawings. Please note that the embodiments described below do not limit the scope of the invention as claimed, and not all of the elements and combinations thereof described in the embodiments are necessarily essential to the solution of the invention.

A work performance analysis system and a work performance analysis method according to a first embodiment of the present invention will be described with reference to Figures 1 to 9. Figure 1 is a diagram showing an example of the configuration of a work performance analysis system according to a first embodiment of the present invention.

The work performance analysis system 1, which is configured using a computer, includes, as an example of the system configuration, an input unit 11, a work performance database DB1, a work location database DB2, a work performance analysis unit 13, a work performance analysis results database DB3, and a display unit 18.

Figure 2 shows an example of worker location information D11 recorded in the work performance database DB1. Worker location information D11 includes at least time information D11a and location (location) information D11b. Time information D11a indicates the time the worker was at the relevant location, and preferably includes information on the time the worker entered the relevant location (e.g., the worker's office) (8:50) and the time the worker left the location (9:15). Location information D11b indicates the locations where the worker stayed, such as the worker's office, supervisor's office, corridor (corridor 1, 2), changing room, and workroom (workroom 1, 2). The location resolution should be considered based on the resolution required for the analysis. For example, for important buildings, it is desirable to record which room the worker was in within the building, but for less important buildings, it is sufficient to simply record that the worker was in the building.

The worker location information D11 shown in Figure 2 is obtained for each worker, and according to this, the movement history of a specific worker by time on a specific day is recorded and understood as location and time information. As a result, the movement history of all workers is understood. However, the times here are understood as the times of entry and exit to the relevant location.

Figure 3 shows an example of work time information D12 recorded in the work performance database DB1. Work time information D12 is information that records the work time of a worker, and includes at least time information D12a and information D12b indicating that work was in progress.

Information D12b indicating whether work was in progress may be a flag only, or may indicate the duration of work by associating the work name or work ID with the time. Time information D12a indicates the time the worker was working, and preferably includes information on the time the work in question (Work 1) started (9:55) and the time the work was completed (10:28).

Depending on the structure of the work performance database DB1, the worker location information D11 in Figure 2 and the work time information D12 in Figure 3 may be in a combined table rather than separate tables. Furthermore, this information may be in a format other than a table, as long as it is in a format that allows reference to the location information D11 and work time information D12.

The location information D11 and work time information D12 recorded in the work performance database DB1 may record the work performance of all workers involved in the work being analyzed, or may record the work performance of only specific workers, such as recording the work performance of one person per work team. Furthermore, the activity performance of not only workers but also supervisors, etc., may be recorded. For example, in the case of supervisors, their work includes witness inspections, and analyzing the supervisor's activity history may be useful for analyzing and improving work such as regular inspections.

Figure 4 shows an example of information recorded in the work location database DB2. The work location database DB2 includes at least information D2a, such as names and numbers that can identify locations as work location data D2, and information D2b that indicates whether each location is a work location.

It is desirable that the information D2 recorded in the work location database DB2 be information that covers all the locations recorded in the location information D11 shown in Figure 2 (worker offices, supervisor offices, corridors, changing rooms, work rooms, etc.).

Information D2b also describes whether the location is a work location or not, for example, using Yes or No. In the example of Figure 4, rooms, conference rooms, break rooms, changing rooms, corridors, worker offices, supervisor offices, etc. are not considered to be work locations, and work rooms are defined as work locations.

Next, we will explain the processing of the work performance analysis unit 13. This processing is divided into two stages: an accumulated data processing stage and a work performance analysis stage. Figure 5 shows the processing flow of the work performance analysis unit 13, mainly the accumulated data processing stage.

In the accumulated data processing stage of the work performance analysis unit 13 shown in Figure 5, first in processing step S1, the worker's location information D11 recorded in the work performance database DB1 is imported as chronological information. Figure 6a is a diagram showing the location information D11 of Figure 2 in chronological order. After clocking in, this worker performs his or her work by moving sequentially from the worker's office, supervisor's office, workroom 1, and workroom 2.

Next, in processing step S2, work location information D2 recorded in work location database DB2 is imported as chronological information, and the work location data D2 is reflected in chronological order in the chronological location information D11 obtained in processing step S1. Figure 6b is a diagram showing the work location data D2 reflected in chronological order in location information D11. According to this, the worker's office and supervisor's office are not defined as work locations (hereinafter referred to as non-work locations), so they are shown in white on the illustration, while Workroom 1 and Workroom 2 are defined as work locations, so they are shown with a background color on the illustration, indicating that the work location data D2 has been reflected in location information D11.

Furthermore, in processing step S3, the work time information D12 recorded in the work performance database DB1 is imported as chronological information. Figure 6c is a diagram showing the work time information D12 of Figure 3 in chronological order. This worker performed work 1, work 2, work 3, and the witnessed inspection in that order.

Comparing time D11a in Figure 2 and time D12a in Figure 3 from the perspective of work, the worker entered workroom 1 at 9:53 and left at 10:30, but the actual work took place from 9:55 to 10:28, so the net working hours were calculated in processing step S3. In Figure 6c, only the net working hours are displayed chronologically.

As can be seen from Figure 6c, net working hours are the time spent working on-site, while the other time is spent preparing for on-site work, in meetings, and traveling; this invention aims to analyze work performance including these time periods.

Processing step S4 in Figure 5 shows the work performance analysis stage, which involves overlaying Figure 6b and Figure 6c, and overlaying information on whether the worker was in a work area or a non-work area, and whether the worker was working, to represent the behavioral history in four categories. This process is shown in detail in Figure 7. Note that the processing procedure in Figure 7 is just one example, and the processing procedure in Figure 5 may be carried out in succession. There are various implementation methods, and one example is described here.

In the process shown in Figure 7, first, in processing step S11, the worker is identified, then in processing step S12, the time is sequentially specified, and in processing step S13, the work time information D12 recorded in the work performance database DB1 is referenced.

In processing step S14, it is determined whether the work time information D12 indicates that work is in progress (yes) or not (no), and in each case, in processing step S15, the worker location information D11 recorded in the work performance database DB1 and the work location information D2 recorded in the work location database DB2 are referenced.

If the determination in processing step S14 is that work is in progress (yes), the work location is confirmed in processing step S16, and if it is a work location (yes), this time is defined as category 4 in processing step S18. The work location is also confirmed, and if it is not a work location (no), this time is defined as category 2 in processing step S19.

Furthermore, if the determination in processing step S14 is that the person is not working (No), the work location is confirmed in processing step S17, and if it is a work location (Yes), this time is defined as Category 3 in processing step S20. Similarly, the work location is confirmed, and if it is not a work location (No), this time is defined as Category 1 in processing step S21.

In processing step S22, the time is changed and updated, and processing returns to processing step S12 to confirm the activities for that day. Similarly, in processing step S23, the worker is changed and updated, and processing returns to processing step S11 to confirm the activities of all target workers.

Figure 6d shows an example of the classification results. Each time period is assigned one of the following classifications.

As described above, the analysis method according to Example 1 of the present invention classifies the behavioral history of a worker into four patterns depending on whether the worker was working and whether the worker was located at a work site. Whether a worker was working at a certain time is confirmed using the work time information D12 in the work record database DB1. Whether the location where the worker was located was at a work site is confirmed using the location information D11 in the work record database DB1 and the work site database DB2.

Figure 8 is a diagram showing classification patterns. For convenience, we will use "Category 1" to refer to when a worker is not working and not at the work location, "Category 2" to refer to when a worker is working but not at the work location, "Category 3" to refer to when a worker is not working but at the work location, and "Category 4" to refer to when a worker is working and at the work location. Below we will explain what can be inferred by classifying a worker's behavioral history into these four patterns.

Category 1 indicates that the worker was not working and was not at the work site, so they were engaged in activities other than work, such as attending a meeting or traveling. Category 2 indicates that the worker was working outside the work site, so it can be inferred that this was likely time spent performing unplanned work. For example, the previous work team's work could have continued for a long time, making the work site unusable, so the worker was forced to work outside the work site, or they may have performed emergency repairs outside the work site to address a malfunction.

Category 2 actions are unlikely to have been planned, and so are items for which work improvements should be made by later analyzing what was being done through interviews or other methods. Category 3 refers to time when a worker is in the work area but not working, and can be estimated as time when the worker is likely to have been forced to wait for some reason. For example, a worker may be waiting because their next task cannot begin until another worker in another work area has finished work, or waiting because the supervisor for an on-site inspection has not yet arrived. Here, depending on the task definition, work preparation and cleanup could also fall into Category 3.

However, to fully utilize the benefits of the classification according to this embodiment, it is desirable to take measures to prevent work preparation and tidying up from being classified as Category 3. Specifically, if the preparation and tidying up of a work are simple tasks, the series of actions including preparation and tidying up should be defined as the work. Alternatively, if the preparation or tidying up is highly important and should be analyzed in detail, then preparation alone or tidying up alone should be considered a single task and managed using work time information D12. By taking such measures, it can be estimated that Category 3 is a time when it is highly likely that the worker was forced to wait for some reason.

Category 4 is time when a worker was working at a work location, and is time when it is highly likely that the planned work was being performed. Furthermore, if there are multiple work locations, it may be determined whether the work location was a specified one. Furthermore, the level of detail in the analysis and the amount of data acquired will vary depending on the definition of the work. For work that requires detailed analysis, the method of this embodiment allows for detailed analysis by treating each of the multiple tasks divided into separate tasks as a separate task and recording the information in the work performance database DB1, but this increases the effort required to acquire data. On the other hand, for work groups that are not highly important to analyze, combining multiple tasks into a single task can reduce the amount of data acquired and the effort required for analysis.

As described above, by classifying time based on whether the worker was working or not, and whether the worker was located at the work site or not, it can be easily estimated that Category 1 is likely to be time spent on non-work activities such as meetings or travel, Category 2 is likely to be time spent on unplanned work, Category 3 is likely to be time spent on waiting, and Category 4 is likely to be time spent on planned work. The classification results are stored in the work performance analysis results database DB3.

Figure 9 shows an example of a method for displaying the analysis results of a worker's performance as a graph. By visualizing the analysis results on the display unit 18 as a graph, it is easy to see which classifications took the longest time.

Instead of visualizing the analysis results as a pie chart like the one shown in Figure 9, a visualization method that includes time information, such as a Gantt chart, can be used to show which category of actions were performed at each time point, or a bar graph that makes it easy to understand the action results of each worker, or a non-graphical display method such as text can also be used.

Furthermore, by performing an analysis that adds information relating characteristics such as job title and affiliated company to the work performance database DB1, it is possible to visualize classifications by job title and affiliated company. Furthermore, for category 4, if the type of work is also clear from the work time information D12, a graph showing the time required for each work task can be displayed in addition to the notation shown in Figure 6.

Furthermore, since the gist of this invention is to analyze behavioral performance, even if the display unit is not included in the work performance analysis system 1 and is visualized by a different system based on the information output by the work performance analysis system 1, this still falls within the scope of this invention.

By performing the analysis shown in this example, it is easy to determine which categories require the longest time, or which categories have the largest discrepancies from the planned time. This makes it possible to obtain information for implementing measures to reduce the time required for non-work activities and secure working time during periodic inspections of power plants, as well as for improving work processes and work plans.

In addition, recording all of a worker's actions could be a burden on the worker and the recorder, but the method shown in this example limits the information required to analyze a worker's behavioral history, making it a highly practical method.

Example 2 of the present invention will be described using Figures 10 to 13. Example 2 is the same as Example 1 except for the points described below.

Figure 10 shows an example configuration of a work performance analysis system 1 according to a second embodiment of the present invention. In addition to the configuration of the first embodiment, the work performance analysis system 1 of the second embodiment includes a non-work time database DB4 and a work performance detail analysis unit 16.

Figure 11 is a diagram showing an example of non-working time information D4 recorded in the non-working time database DB4. The non-working time database DB4 is used to perform a more detailed analysis of the contents of categories 1 and 3 in Example 1. The non-working time database DB4 includes at least information D4a such as names and numbers that can identify locations (offices, break rooms, changing rooms, corridors, work rooms, etc.), and information D4b on activities that workers are likely to be engaged in when they are in each location and not working (waiting in the office, meetings, taking breaks, changing clothes, traveling, waiting at the site, etc.).

The information recorded in the non-working time database DB4 is preferably information that covers all the locations recorded in the location information D11 shown in Figure 2. Here, if there are multiple workers and each worker's behavior during non-working time at each location differs, the database may contain different information for each worker. Furthermore, in this invention, the term "worker" may also refer to someone other than the worker involved in the work being analyzed, such as a supervisor D4c.

Figure 12 shows an example of a flow for selecting candidate activities in non-work areas. In the example shown, whether it is a break time is confirmed in processing step S31, and if the answer is Yes, it is defined as a break time in processing step S32. If the answer is No, in processing step S33, it is appropriately defined as an activity other than a break, for example by referring to the non-work time database DB4.

In addition, if there are multiple candidates for activities during non-work time at a location defined as rest time, the non-work time database DB4 may have multiple candidates for one location, and may also have conditions for determining which of the multiple candidates is appropriate.

For example, multiple candidates could be when a worker is in the office but not working, with one candidate being waiting in the office and the other being a break. Furthermore, the condition for making the determination could be, for example, whether the time being analyzed is during a break, and if it is during a break, the worker's behavior is likely to be a break; if it is not during a break, the worker's behavior is likely to be an activity other than a break as listed in the non-work time database DB4.

In Figure 10, for cases classified into Category 1 and Category 3 in Example 1, the work performance detail analysis unit 16 performs a more detailed analysis of the time spent by workers on non-work activities based on the location information D11 in the work performance database DB1 and the information in the non-work time database DB4, based on the location of the worker when not working.

Figure 13 shows an example of a method for displaying a breakdown of non-work time as a graph. By using the non-work time database DB4, it is possible to estimate and visualize the behavior of workers during non-work time in more detail. This makes it easier to analyze which parts of the time required for non-work activities need to be reduced in order to secure more working time. Also, while Figure 13 shows the results of analyzing all non-work time together, it is also possible to display the results of analyzing category 1 and category 3 separately from Example 1.

A work performance analysis system according to Example 3 of the present invention will be described using Figures 14 to 20. Example 3 is the same as Example 1 or Example 2, except for the points described below. In this example, the worker who is the main subject of analysis is Worker A, and the people who have an influence on Worker A's work are Worker B and a supervisor.

Figure 14 shows an example configuration of a work performance analysis system 11 according to Example 3. In addition to the configuration of Example 2, the work performance analysis system 1 of Example 3 includes a work start condition database DB5, and is used to analyze the contents of Category 3 in Examples 1 and 2 in more detail.

Figure 15 is a flowchart showing a series of analytical processing examples in Example 3. Of these, the processing up to processing steps S3 and S4 shown in the upper part is performed using the same concept as in Figure 5, and here it is assumed that the work has already been divided into categories 1, 2, 3, and 4. Furthermore, it is assumed that categories 2 and 4 are already the subject of separate analysis as unplanned work or work time information, respectively.

Furthermore, category 1 is processed according to Example 2. For example, in processing step S41, information classified as category 1 is read from the non-work time database DB4, and in processing step S42, the behavioral history during non-work time is estimated by the processing shown in Figure 12. In processing step S43, the information displayed in the graph in Figure 13 is obtained as the detailed analysis result of category 1.

In Example 3, processing is performed on information in Category 3 in accordance with the conditions described in the work start condition database DB5. First, in processing step S51, information classified as Category 3 is read from the work start condition database DB5.

Figure 16 shows an example of information recorded in the work start condition database DB5. The work start condition database DB5 includes at least information such as names and numbers that can identify the work (work name D5a) and work start conditions for each work (D5b, D5c, D5d).

Work start conditions are the conditions that must be met for worker A to begin the next task. For example, worker A must be present at the work site, worker A's previous task must be completed, worker B's related task must be completed, or in the case of an on-site inspection, the supervisor must have arrived at the work site.

It is desirable that the work start conditions can be confirmed as being met based on the information recorded in the work performance database DB1. For example, the presence of worker A and a supervisor at the work site can be confirmed using location information D11, and the completion information of worker A's own work and the completion information of related work by worker B can be confirmed using work time information D12.

In Figure 15, category 3 is determined in processing step S51 to extract and process only information that meets the work start conditions, but the basic concept is the same as category 1. For example, in processing step S41A, information that falls into category 3 and meets the work start conditions is read from non-work time database DB4, and in processing step S42A, the behavioral history during non-work time is estimated using the processing in Figure 17, and in processing step S43A, the information displayed in the graph in Figure 18 is obtained as the detailed analysis result for category 3.

Figure 17 is a diagram showing an example of a flow for selecting candidates for non-work activities at a work site. It shows the detailed processing content of processing step S42A in Figure 15. In this processing flow, processing steps S61, S62, and S63 individually confirm whether "work start conditions 1, 2, and 3 are met," and if they are not met, processing steps S62, S64, and S66 respectively determine "waiting due to work start conditions 1, 2, and 3." In other words, for the work being analyzed, it is determined whether the work start conditions listed in the work start condition database DB5 were met at a certain time.

If the determination in processing step S67 is that "even one of the work start conditions has not been met," it is determined that the worker was waiting at the work location because the work start condition had not been met, and processing step S69 determines that the worker was "waiting due to work start condition." Note that in the determination in Figure 17, there may be one or more work start conditions that have not been met.

On the other hand, if all work start conditions are met, it can be assumed that the behavior was recorded in the non-work location database DB4 based on the worker's location, and processing is performed to reference the non-work location database DB4 in processing step S68. Among the work start conditions, the worker's own presence in the work location or the worker's completion of the previous process are work start conditions, but do not cause the worker to wait. Therefore, among the work start conditions, conditions based on the worker's own behavior do not need to be considered as causes of the worker to wait.

Figure 18 shows an example of a method for displaying a breakdown of non-work activities in a workplace as a graph. Because multiple work start conditions may not be met at the same time, the graph can be devised to show the wait time for each item for a single work start condition, and to aggregate the wait time for two or more work start conditions by the number of wait factors. Wait time for a single work start condition can be eliminated by eliminating the corresponding factor, so the benefits obtained are significant relative to the cost of the countermeasure.

On the other hand, waiting due to multiple work start conditions requires addressing all of the causes of the wait, which means the wait time cannot be eliminated, and the cost of countermeasures is high. Note that wait time not dependent on work start conditions refers to the case in the flowchart of Figure 17 where all work start conditions are met, and is calculated by classifying non-work activities according to the worker's location using the non-work location database DB4. This graph may be the result of an analysis of a single worker, or it may be a combined display of the analysis results of multiple workers.

As described above, according to the method described in Example 3, by performing analysis using the work start condition database DB5, it is possible to perform a detailed analysis of the breakdown of cases in Category 3 where a worker was engaged in activities other than work at the work site. It is also possible to visualize which waiting factors can be resolved to have the greatest effect on improving work efficiency.

Figure 19 is a graph showing the results reflecting all of the analysis methods described so far. In addition to the four classifications in Example 1, detailed analysis of Classes 1 and 3 was performed in Example 2, and further detailed analysis of Class 3 was performed in Example 3. Furthermore, Class 4 is clear from the work time information D12.

Although not mentioned in the examples of the present invention, Category 2 may also be analyzed in more detail using some method. For example, analysis could be performed based on the timing of work performed outside the work area. If the work in question is Work 4, and Work 4 was first performed outside the work area, it is highly likely that Work 4 was performed outside the designated work area because that work area was occupied by another worker. Furthermore, if Work 4 was performed in the designated work area and then resumed outside the designated work area, one possible analysis method would be that there is a high probability that Work 4 was defective and that the worker performed rework in an empty space.

Figure 20 shows an example of a method for displaying the results of a detailed analysis of action performance as a timing chart. Here, the analysis focuses on worker A, and also displays the action history of worker B and the on-site inspection supervisor as people who may influence worker A's work. By displaying actions 34 along the time axis as in Figure 20, it is possible to clearly show the waiting time 36 that worker A had to wait due to work start conditions 35, such as worker B's completion of task B1 or the arrival of the on-site inspection supervisor. Furthermore, because the duration of each action 34 is proportional to the time required for each action, it is also possible to grasp the actual man-hours involved.

Although Examples 1, 2, and 3 of the present invention have been described above, the examples do not limit the invention according to the claims, and not all of the elements and combinations thereof described in the examples are necessarily essential to the solution of the invention. For example, each database used in the present invention does not need to be an independent database; as long as it can provide the information described in the present invention, it may be combined with a database described in the present invention, or with a database not described in the present invention. Furthermore, in some expressions in the present invention, the combination of work and non-work activities is referred to as "behavior," but the terms "work" and "behavior" may be used interchangeably as long as interpretation is not compromised. Furthermore, the scope of the present invention is not limited to maintenance work; worker behavior can be analyzed using the method described in the present invention in manufacturing, construction, and other industries as well.

Apart from various databases that can be prepared beforehand or afterward, the only information required for the above analysis is the work performance database DB1, which stores information on the location of workers at each time point and the amount of time they spent working. Therefore, the number of measurement items and measurement frequency can be kept to a minimum, making this a method that places less of a burden on workers and measurers.

1...Work performance analysis system DB1...Work performance database DB2...Work location database DB3...Work performance analysis result database DB4...Non-work time database DB5...Work start condition database 13...Work performance analysis unit 16...Work performance detail analysis unit 18...Display unit D11...Location information D12...Work time information 31...Worker A
32...Worker B
33... On-site inspection supervisor 34... Action 35... Work start conditions 36... Waiting time

Claims (14)

A work performance analysis system comprising a work location database that stores information indicating whether each location is a work location, and a work performance database that stores information on the location of workers at each time and the amount of time they spend working, and using the information in the work location database and the work performance database , classifying the worker into four cases by combining two cases: when the worker is at a designated work location or not, and two cases: when the worker is working or not working, and tallying up the time taken by the worker for each category, and estimating the worker's behavioral history. The work performance analysis system according to claim 1,
This work performance analysis system has a non-working time database that records candidate actions for when a worker is not working at each location, and uses the non-working time database to perform detailed analysis of Category 1, where the worker is not at a designated work location but is working, and Category 3, where the worker is at a designated work location but is not working . The work performance analysis system according to claim 2 ,
A work performance analysis system , wherein the non-work time database is created for each of a plurality of workers . The work performance analysis system according to claim 2 ,
A work performance analysis system characterized in that the non-work time database can record a plurality of candidate actions for one worker at one location. The work performance analysis system according to claim 4 ,
A work performance analysis system characterized in that the non-work time database can record criteria for prioritizing the plurality of candidate actions described in the non-work time database . The work performance analysis system according to claim 2 ,
A work performance analysis system characterized by having a work start condition database that records work start conditions, which are conditions under which a worker can start a specified task, and performing detailed analysis of category 3 by using the work start condition database to estimate the factors that caused a worker to wait . The work performance analysis system according to claim 6 ,
A work performance analysis system, wherein the work start conditions recorded in the work start condition database include completion of work by other workers. The work performance analysis system according to claim 6 ,
A work performance analysis system having a non-work time database that records possible actions that a worker might take when not working at each location, and when the reason the worker is not working is not due to the work start conditions, the system estimates the worker's actions using the non-work time database . The work performance analysis system according to claim 6 ,
A work performance analysis system that has the function of determining how many work start conditions have caused a worker to wait at any given time . The work performance analysis system according to claim 1 ,
A work performance analysis system comprising a display unit that displays the analysis results of a worker's behavior history . The work performance analysis system according to claim 10 ,
A work performance analysis system characterized in that the analysis results displayed on the display unit display the behavioral histories of multiple workers on a time axis . A work performance analysis method using a computer that uses a work location database that stores information indicating whether each location is a work location, and a work performance database that stores information on the location of workers at each hour and the amount of time they spend working, to classify into four cases by combining two cases: when the worker is at a designated work location or not, and two cases: when the worker is working or not working, and to tally up the time taken by the worker for each category, and to estimate the worker's behavioral history . The work performance analysis method according to claim 12,
The computer utilizes a non-working time database that records possible actions for when a worker is not working at each location, and uses the non-working time database to perform a detailed analysis of Category 1, where the worker is not at the designated work location but is working, and Category 3, where the worker is at the designated work location but is not working . The work performance analysis method according to claim 13,
The computer has a work start condition database that records work start conditions, which are conditions under which a worker starts a specified task, and performs the detailed analysis of Category 3 by using the work start condition database to estimate the factors that caused the worker to wait .