JP2009289134A - Work analyzer, production management method and production management system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To evaluate the state of each worker at a production site, and to attain work efficiency improvement. <P>SOLUTION: In a technique for analyzing work records including an identifier of a worker who worked, a work type indicative of the type of work, a work start time and a work duration time, for each work on a commodity in a production site, a work record corresponding to the worker and work type is extracted; the statistical value of the work duration time is calculated for each predetermined time period, regarding each extracted work record; and work efficiency of the work is evaluated for each worker based on the calculated statistical value. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a production management technique at an article manufacturing site, and more particularly to a technique for indexing and evaluating work efficiency.

  In manufacturing sites or factories that produce articles, there is a constant need to optimize and streamline manufacturing processes and individual operations. Therefore, it is necessary to accumulate, monitor and evaluate the performance of various factors during the manufacturing process and work.

  However, in order to acquire, collect, and monitor the work performance at the manufacturing site as data, appropriate equipment is required at the manufacturing site or the factory. Further, depending on the accuracy of the data to be collected, it may be necessary to add more equipment, which increases the necessary cost. Furthermore, there are cases where it is necessary to change conventional operations and operations for data acquisition and collection, which makes introduction difficult.

  Further, in the present situation where the effectiveness of a production form called cell type production is being recognized, it is particularly important to collect and monitor data related to the work of an operator who is the core of cell type production.

  On the other hand, the technology for monitoring the manufacturing dynamics of articles at the manufacturing site can acquire and collect manufacturing dynamics information and trace information without bothering workers by introducing individual product recognition technology such as RFID. It has become. For this reason, collecting and accumulating information related to the work of workers using the individual product recognition technology has been put into practical use in various forms.

  As a method of using information collected by the individual product recognition technique, for example, a technique for measuring the skill level of an operator by measuring the time during which the operator does nothing is disclosed (patent) Reference 1). In addition, a technique for evaluating a manufacturing site by evaluating collected information quantitatively and qualitatively is disclosed (see Patent Document 2). Furthermore, a technique is disclosed in which the time required for work is totaled for each work type and a reference value for the number of workers required for each work is calculated (see Patent Document 3).

Furthermore, a technique for analyzing work efficiency of a plurality of workers, separating a good worker from a non-excellent worker, and a technique for specifying a fixed work with room for improvement are disclosed (see Patent Document 4). Further, a technique for simulating abnormal data or the like based on collected information is disclosed (see Patent Document 5).
Japanese Patent Laid-Open No. 08-055103 JP 2004-102325 A Japanese Patent Laid-Open No. 05-197727 JP 2006-260156 A JP 2007-140625 A

  In manufacturing sites where human work is important, it is particularly important for optimizing the manufacturing process to understand and monitor work information such as the work quality, work level, and work status of each worker. . However, it is difficult to introduce new equipment and systems to the manufacturing site in order to acquire and collect the work quality, work level, and work status of each worker due to cost problems. For example, in order to apply the technique disclosed in Patent Document 1 to a manufacturing site, a means for further subdividing the work and measuring a blank time between work of smaller units is necessary, and the means is realized. This is often difficult.

  In addition, even if there is a means for acquiring or collecting work information, the work quality, work level, or work state of each worker is used as an objective and quantitative index value. Is difficult. For example, in the technique disclosed in Patent Document 2, there is a high possibility that human subjectivity will be included particularly during qualitative evaluation, and it is difficult to say that it is an objective index. There is the work speed of each worker as a numerical value that can be such an objective index, but objectively and logically the criteria of how much work speed is judged as "work is fast" or "work is slow" in the first place. It is an extremely difficult problem to set, and the current situation is that it is set individually based on the knowledge of the operator or work supervisor taking into account the circumstances at each site.

  Furthermore, if only the work speed of each work is used as an index, the manual work is affected by various accidental factors, so the stability as data is low. In addition, when data is collected for a certain number of data and for a certain period, and an index is calculated for each worker based on the average value of work speed, the average value includes a large value in the original data due to the occurrence of an accident during work, etc. In some cases, the smaller the overall value, the stronger the effect, and the lower the reliability of the data representing the overall trend. In addition, when the maximum value of work speed is used as an indicator for each worker, the indicator may be determined by the highest value measured accidentally, and in order to obtain the maximum value only for a specific time zone. Since the work speed can be increased and the work speed can be slow at other times, the reliability as an objective index is low.

  Moreover, even if the work speed or work efficiency of each worker can be evaluated by comparison with other workers by the technique disclosed in Patent Document 4, in order to actually function the manufacturing site, It is impossible to have only the workers who are evaluated as excellent work. In fact, it is not a fundamental solution because all workers need to be utilized according to their capabilities and conditions.

  In one form of this invention, it is the operation | work analysis apparatus which analyzes the operation | work performance recorded for every operation | work with respect to goods in a manufacturing site, Comprising: The processor which performs the process which analyzes the said operation | work performance, It connects to the said processor. Each of the work results includes an identifier of the worker who performed the work, a work type indicating the type of the work, a work start time, and a time required for the work. And a work result having the same work type is extracted, and for the extracted work result, a statistical value of the work required time is calculated for each predetermined period, and the statistical value calculated for each predetermined period is calculated. Based on this, the work efficiency of the work is evaluated for each worker.

  According to a typical embodiment of the present invention, the ability and state of each worker can be evaluated by calculating a statistical value based on the work performance for each worker.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

(First embodiment)
At the manufacturing site that is the subject of the present invention, a tag such as an electronic tag (for example, RFID) is given to a product and a worker, and an electronic tag reader is installed at a work place in each work process. Collect manufacturing dynamics information.

  The production dynamic information is acquired and accumulated based on the work results at the production site. For example, the identification information of each product, the identification information of the process in which the product was subjected to the manufacturing process, the time (start time, end time) and time required for the manufacturing process in the process, the time required for the work This is individual work history information including the identification information of the worker who performed the operation. Manufacturing dynamic information is originally collected and accumulated in all product manufacturing processes, and is used for monitoring and managing product manufacturing dynamics. A specific example of the production dynamic information will be described later with reference to FIG.

  In the first embodiment of the present invention, statistical processing is performed for each worker on the production dynamic information of each work of the worker, and an index representing the state of the worker is calculated. Specifically, the worker's state is the quality of work performed by the worker. In the first embodiment, attention is paid to the fact that the work speed or the like cannot be kept constant when the work quality is deteriorated due to fatigue or the like. Specifically, the time (work required time) required for each work of each worker is classified for each product type or work type to be worked on, and the variance or standard deviation of work time in a specific period The value is used as the index value as the degree of variation. Then, based on the acquired index values of individual workers, the workload is adjusted according to the capabilities or conditions of the workers without introducing new equipment, etc. Perform optimization.

  FIG. 1 is a diagram illustrating an example of a production management system according to the first embodiment of this invention.

  The production management system according to the first embodiment of the present invention includes a local terminal 117, a local terminal 105, a work performance collection server 110, a work performance storage database (DB) 111, a work analysis server 112, a worker information storage database 113, And a work plan management server 114. The local terminal 117, the local terminal 105, the work record collection server 110, and the work plan management server 114 are connected to each other via a network.

  Further, at the manufacturing site according to the first embodiment of the present invention, each worker is provided with a worker tag 101 such as an electronic tag in which personal identification information 102 is described, and individual identification information is assigned to a product to be worked on. An article tag 103 describing 104 is attached.

  The local terminal 117 and the local terminal 105 are arranged at a work place in each work process. The local terminal 117 is a terminal on which work instructions are displayed based on the work plan drafted by the work plan management server 114. On the other hand, the local terminal 105 is a terminal for reading the electronic tag and transmitting the manufacturing activity information to the work performance collection server. Note that the local terminal 117 and the local terminal 105 may be the same terminal.

  The local terminal 117 includes a CPU 131, an interface (I / F) 132, and a memory 133.

  The CPU 131 executes business processing by processing a program stored in the memory 133. The interface 132 is connected to the work plan management server 114 and the like via a network.

  The memory 133 stores a program executed by the CPU 131, data necessary for executing the program, and the like. Specifically, the memory 133 stores a program for executing the work instruction reception processing unit 118 and the work instruction presentation processing unit 119.

  The work instruction reception processing unit 118 is a process of receiving the work instruction transmitted from the work plan management server 114 via the interface 132. The work instruction presentation processing unit 119 is a process for presenting the received work instruction to the worker.

  The local terminal 105 includes a CPU 134, an interface 135, a clock 109, a tag reading device 137, and a memory 136.

  The CPU 134 executes business processing by processing the program stored in the memory 136. The interface 135 is connected to the work record collection server 110 and the like via a network. The tag reading device 137 reads information from the electronic tag.

  The memory 136 stores a program executed by the CPU 134 and data necessary for executing the program. Specifically, the memory 136 stores a program for executing the work record transmission processing unit 108 and the tag reading processing unit 106. Further, the work record transmission processing unit 108 stores work identification information 107 transmitted to the work record collection server 110.

  The local terminal 105 reads the personal identification information 102 from the worker tag 101 by the tag reading device 137 and the tag reading processing unit 106. Further, the individual identification information 104 of the article is read from the article tag 103 attached to the work target article.

  The work record transmission processing unit 108 generates work identification information 107 based on the personal identification information 102 and the individual identification information 104 read by the tag reading device 137 and the tag reading processing unit 106. Further, the work identification information 107 and the time information acquired from the clock 109 are transmitted to the work result collection server 110 as manufacturing activity information. Hereinafter, a procedure for transmitting the work identification information 107 to the work record collection server 110 in each work process will be specifically described.

  First, in the local terminal 105, when the article tag 103 attached to the product or the like is first recognized by the tag reading processing unit 106 via the tag reading device 137, work on the article at the place is started. And Further, it is assumed that when the article tag 103 of the article is no longer recognized, the work on the article at the location is finished.

  At this time, the personal identification information 102 is read from the worker tag 101 of the worker who is performing the work, the work identification information 107 is generated, and the work start date / time and the work end date / time are set to the clock 109. And is transmitted to the work record collection server 110 as the production dynamic information of the work.

  The work result collection server 110 is a computer that collects and stores work results (manufacturing activity information). The work record collection server 110 is connected to the work record accumulation database 111. The work record collection server 110 receives the manufacturing activity information transmitted from the local terminal 105 and stores it in the work record accumulation database 111.

  The work performance collection server 110 includes a CPU 147, an interface 148, and a memory 149.

  The CPU 147 executes business processing by processing the program stored in the memory 149. The interface 148 is connected to the local terminal 105 or the like via a network.

  The memory 149 stores a program executed by the CPU 147, data necessary for executing the program, and the like. Specifically, the memory 149 stores a program for executing the work result reception processing unit 161 and the work result storage processing unit 162.

  The work record reception processing unit 161 receives the manufacturing activity information transmitted from the local terminal 105. The work record accumulation processing unit 162 accumulates the received manufacturing activity information in the work record accumulation database 111.

  The work analysis server 112 analyzes the work status of each worker by performing statistical processing on the data stored in the work performance accumulation database 111. The analysis result is accumulated in the worker information accumulation database 113 as analysis information for each worker.

  The work analysis server 112 includes a CPU 144, an interface 145, and a memory 146.

  The CPU 144 executes business processing by processing the program stored in the memory 146. The interface 145 is connected to the work plan management server 114 and the like.

  The memory 146 stores a program executed by the CPU 144 and data necessary for executing the program. Specifically, the memory 149 stores a program for executing the statistical processing unit 163 for analyzing the manufacturing movement information. Details of the statistical processing will be described later.

  The work plan management server 114 manages a work plan at the manufacturing site. Specifically, the work plan is adjusted or corrected in real time on the basis of the analysis information of the manufacturing movement information stored in the worker information accumulation database 113, and the work plan is drawn up. Based on the work plan that has been adjusted or corrected, or the work plan that has been prepared, a work instruction is transmitted to the local terminal 117 at each work place.

  The work plan management server 114 includes a CPU 141, an interface 142, and a memory 143.

  The CPU 141 executes business processing by processing a program stored in the memory 143. The interface 142 is connected to the local terminal 117 or the like via a network.

  The memory 143 stores a program executed by the CPU 141, data necessary for executing the program, and the like. Specifically, the memory 146 stores a work plan planning processing unit 115 and a work plan adjustment processing unit 116.

  The work plan creation processing unit 115 newly drafts a work plan at the manufacturing site based on the analysis information of the manufacturing movement information stored in the worker information accumulation database 113. The work plan adjustment processing unit 116 corrects or adjusts an already planned work plan based on the analysis information of the manufacturing movement information stored in the worker information accumulation database 113.

  The manufacturing movement information collected by the production management system described above includes identification information of the worker who performed the work. Therefore, the accumulated production dynamic information can be organized as information for each worker, and can also be used as production dynamic information for each worker.

  In addition, for each work type of each worker, for each work type such as each process type, calculate the variance of the actual value of the work required time or standard deviation value in any time interval or period, The calculated variance value or standard deviation value is used as an index value as the degree of variation in the work required time for each work type of each worker during a certain period.

  Normally, when the work quality deteriorates due to factors such as worker fatigue accumulation, the time required for work becomes unstable and varies. Therefore, the degree of variation in work time is used as an index value for determining the worker's fatigue level. Similarly, it is used as an index value for measuring a change in work quality of the worker.

  In addition, the distribution of the actual value of the work required time in any time interval or period of the previous operation, specifically, the minimum value in the history of all the work required time (that is, at the highest effective efficiency) Calculate the ratio of the actual value included in a specific numerical value range near the minimum value of the work required time, and how often the actual value exists near the work required time corresponding to the work processing result) The value of the ratio obtained is used as an index value as the degree of stability of the work required time.

  The degree of stability is also used as an index value for measuring changes in the worker's fatigue level and work quality, similar to the above-described variation degree.

  Further, by calculating each index value of the degree of variation and the degree of stability for each period of a certain amount of short time, such as a unit of several hours or a unit of days, and comparing the relative values, the state of the worker's fatigue degree and the like can be obtained. It is used as the index value shown.

  By extracting, monitoring, and managing the index values extracted as described above at any time period as needed, conditions such as the degree of fatigue or work quality of each worker engaged in work at the manufacturing site To monitor. Then, measures corresponding to the state such as adjustment of the work load as a whole manufacturing site and correction of the manufacturing schedule are carried out to optimize work efficiency as a whole manufacturing site. At this time, the correspondence method and the degree of correspondence may be changed according to the change amount of the index value.

  In addition, the above index values are accumulated as a history of worker status such as the degree of fatigue and work quality, and the trends for each condition such as time zone, day of the week, weather, and production line configuration are organized and extracted. Planning and revision of work plans and production schedules optimized for

  Furthermore, the index values of the degree of variation and the degree of stability are calculated for each worker for a relatively long period such as a few days, a week, or a month, and the workers are compared with each other. It can be used as an index value for measuring the degree of refinement. It is possible to optimize the staffing by taking into consideration the index value of the skill level of the worker when the worker is placed, and to estimate the quality or risk of the production line and the production floor. It can also be used for worker evaluation or educational planning.

  Here, the process of analyzing the production dynamic information will be described. The analysis process of the manufacturing activity information is executed by the work analysis server 112. Further, the analysis information is stored in the worker information storage database 113 as described above. The accumulated analysis information may be monitored by the work plan management server 114 other than the work analysis server 112, or the accumulated analysis information may be used by an external application.

  FIG. 2 is a flowchart showing a procedure for analyzing the production dynamic information according to the first embodiment of this invention.

  This process is executed by the CPU 144 of the work record collection server 110 processing the statistical processing unit 163 that is a program stored in the memory 146 of the work record collection server 110.

  First, the CPU 144 of the work record collection server 110 arranges the manufacturing activity information for each worker (step 201). The manufacturing activity information of the product acquired and accumulated based on the work results at the manufacturing site is accumulated in the work result accumulation database 111 as described above. Hereinafter, an example of manufacturing dynamic information will be described with reference to FIG.

  FIG. 3 is a diagram illustrating an example of the configuration of the manufacturing activity information stored in the work performance accumulation database 111 according to the first embodiment of this invention.

The production dynamic information includes a product ID 401, a process ID 402, a worker ID 403, a work start date and time 404, and a work required time 405. The product ID 401 is an identifier of a work target product. As described above, the product tag 103 including the individual identification information 104 is attached to the product to be worked. The article tag 103 is read by the local terminal 105 and the individual identification information 104 is acquired. The individual identification information 104 includes information such as product identification information.

  The process ID 402 is an identifier of a process in which work is performed. The local terminal 105 that transmits the work results is arranged for each work site corresponding to each process. Therefore, the process identification information may be given by the local terminal 105 that has read the article tag 103 attached to the product.

  The assigned worker ID 403 is an identifier of a worker who is working on the product in the process. Each worker has a worker tag 101 in which personal identification information 102 is recorded. The worker can acquire the identification information of the worker in charge by causing the local terminal 105 to read the worker tag 101 before starting the work.

  As described above, the work start date and time 404 is the time at which the article tag 103 attached to the work target product is first recognized by the local terminal 105. Further, the work required time 405 is calculated from the work start date and time and the work end date and time when the item tag 103 is no longer recognized. Alternatively, the work completion time 405 may be calculated by setting the work end date and time by explicitly inputting the work end to the local terminal 105.

  Here, the description returns to the manufacturing dynamic information analysis process.

  The CPU 144 of the work performance collection server 110 further extracts the work required time for each work type of each worker by rearranging the manufacturing movement information sorted for each worker for each process and each target product. (Step 202). The work type is a work type determined based on the process and the target product.

  The CPU 144 of the work performance collection server 110 collects all the accumulated performance values (the work type of each worker for the entire period) with respect to the collection of the work value of the required time for each work type of each worker acquired and accumulated. The variance value α1 or the standard deviation value β1 is calculated for each work required time) (step 203). Further, a set of all the actual values of the work required time for each work type of each worker is divided at a predetermined sampling interval (for example, 1 hour), and the dispersion value α2 or standard deviation value of the actual values for each divided set β2 is calculated (step 204). The calculated value is stored in the worker information storage database 113 as analysis information of manufacturing movement information. Here, the analysis information of the production dynamic information will be described with reference to FIG.

  FIG. 4 is a diagram illustrating an example of the analysis information of the manufacturing dynamic information stored in the worker information storage database 113 according to the first embodiment of this invention.

  The analysis information of the manufacturing activity information includes a worker ID 501, a process ID 502, a product type 503, statistical processing sampling section information 504, and a statistical value 505.

  As described above, the analysis information of the manufacturing movement information is calculated for each worker and each work type. In FIG. 4, the worker is specified by the worker ID 501, and the work type is specified by the process ID 502 and the product type 503.

  The worker ID 501 is an identifier of a worker who is working at the manufacturing site. The worker ID 501 corresponds to the worker ID 403 in the production dynamic information shown in FIG.

  The process ID 502 is an identifier of a process in which work is performed. The process ID 502 corresponds to the process ID 402 of the manufacturing dynamic information shown in FIG.

  The product type 503 represents the type of product to be worked. The product type 503 corresponds to the product ID 401 of the manufacturing activity information shown in FIG. Note that the product ID 401 and the product type 503 do not necessarily have a one-to-one correspondence. When a product of the same type is operated in the same process, a plurality of product IDs 401 correspond to one product type 503. Also good.

  The statistical processing sampling section information 504 is a period to be analyzed. The statistical processing sampling section information 504 includes a start date and time 511, an end date and time 512, and a section length 513. The section length 513 of the statistical processing sampling section information 504 is a preset value. The start date and time 511 may be specified by the analyst at the time of analysis, or a reference time may be set in advance and the start date and time 511 may be set for each section length 513.

  The statistical value 505 is a result of statistical processing of manufacturing dynamic information corresponding to the period specified by the statistical processing sampling section information 504. In the first embodiment of the present invention, the statistical value 505 includes a variance value 514 for work required time and a standard deviation value 515 for work required time.

  Here, the description returns to the manufacturing dynamic information analysis process.

  The CPU 144 of the work performance collection server 110 detects changes such as increase / decrease and increase / decrease amount of the variance value α2 or the standard deviation value β2 (step 205). Further, it is determined whether or not the variance value α2 at a predetermined sampling interval is larger than the variance value α1 for the entire period, or whether or not the variance value α2 tends to increase (step 206). Note that the determination result of the process in step 206 is the same determination result as that determined by the standard deviation value.

  If the variance value α2 is not greater than the variance value α1 and the variance value α2 does not tend to increase (the result of step 206 is “No”), the CPU 144 of the work record collection server 110 will determine the variance value. Calculation and monitoring of α2 and standard deviation value β2 are continued (step 207).

  When the variance value α2 is larger than the variance value α1 (the result of step 206 is “Yes”), the CPU 144 of the work record collection server 110 determines that the work required time of the worker varies. can do. Therefore, it is determined that the work efficiency of the worker is lowered (step 208), and the worker himself / herself, the work supervisor, or the site manager is notified that the work efficiency is lowered. The countermeasure is taken (step 209). The notification may be displayed on the local terminal 117 together with the work instruction, for example.

  As a countermeasure, the CPU 144 of the work result collection server 110 notifies the work plan management server 114 to reduce the work load of the worker according to the degree of reduction in work efficiency, and the work plan management server 114. May adjust the work plan.

  If only the time required for one operation is long, it is determined that there is a high possibility that it is caused by an external factor such as the occurrence of a sudden accident, and the manufacturing activity information corresponding to the data You may recalculate except. However, if it is determined that the work efficiency has been reduced for a certain length of time, it is considered to be caused by the worker's condition such as fatigue or reduced concentration of the worker. It is determined that the increase in the value α2 is the deterioration of the worker's condition such as fatigue accumulation.

  Furthermore, the variance value α2 at each time interval is compared along the time series, and when the variance value α2 tends to increase with time (the result of step 206 is “Yes”), the state of the worker is It can be determined that it is getting worse (step 208). That is, for example, when fatigue has accumulated over time.

  As in the case described above, the CPU 144 of the work performance collection server 110 notifies the parties concerned that the work efficiency has been reduced, or notifies the work plan management server 114 to take action such as changing the work plan. Or instructing to do so (step 209). When notifying the work plan management server 114, the entire manufacturing site is adjusted so that the load on the worker is reduced by correcting or rescheduling the work plan (for example, the work amount of the worker is reduced). The work load is adjusted, and instructions are given to prevent a decrease in work efficiency as a whole manufacturing site.

  At this time, as a specific countermeasure, for example, when the increase amount of the variance value α2 is large, adjustment is performed such as increasing the amount of reducing the load on the worker. Further, when the increase amount is extremely large, the handling method or the handling degree (load reduction degree, etc.) may be adjusted such that the worker is removed from the production line for a certain period. In addition, when the increase of the dispersion value α2 is not detected in a single shot, but when it is detected continuously a plurality of times, or when the increase of the dispersion value α2 above a predetermined reference value is detected, the state of the operator May be determined to have deteriorated, and the above-described response may be implemented.

  In addition, instead of simply calculating the variance value α2 for each set of actual values divided at regular intervals, the variance value α2 is calculated using the actual value set between the current time and the past one hour as the original data. It may be. In this case, it is possible to observe a change in numerical values with higher real-time characteristics.

  In the first embodiment of the present invention, the history of variance value α2 is accumulated in association with conditions such as time zones, days of the week, dates, and environmental conditions such as weather, and the trend of variance value α2 for each condition Can be analyzed. Therefore, it is possible to grasp the tendency of the state of the worker for each condition such that the work efficiency of the specific worker is lowered or the overall work efficiency is lowered in a specific time zone. And, by creating a work plan based on the grasped trend, it is possible to support the construction of an optimal manufacturing plan that implements load adjustment for the entire manufacturing site or medium- to long-term load / production amount adjustment. it can.

  In addition, by calculating the variance value for each fixed time for each process or for each equipment used in the work, a decrease in work efficiency in a specific process or equipment is detected without depending on the worker, and the process or equipment It is possible to detect deterioration of the image.

  According to the first embodiment of the present invention, conventionally, it is difficult to monitor or evaluate by quantification, the state of individual workers such as fatigue level, or the characteristics of workers such as the level of worker skill Can be converted into an index value quantitatively. And according to various objectives, such as work efficiency optimization, a work plan can be drawn up and adjusted in consideration of an operator's condition or characteristic.

(Second Embodiment)
In the second embodiment of the present invention, as in the first embodiment, product production dynamic information is collected, and the actual time required for each worker's work type is collected from the collected production dynamic information. Get information.

  In the first embodiment, a variance value and a standard deviation value are calculated by statistical processing for the actual work time information, and a decrease in the worker's work efficiency is detected based on the calculated variance value and the like. It was. On the other hand, in the second embodiment, statistical processing is executed based on the case where work is performed with the highest efficiency. The specific contents of the statistical process will be described later.

  In addition, about the content which is common in 1st Embodiment, description is abbreviate | omitted suitably and it demonstrates centering around difference.

  The configuration of the production management system of the second embodiment is the same as that of the first embodiment shown in FIG. The difference is the contents of processing executed by the statistical processing unit 163 which is a program stored in the memory 146 of the work analysis server 112. Other processes such as a process for collecting product manufacturing dynamics information are the same as those in the first embodiment.

  FIG. 5 is a flowchart showing a procedure for analyzing the production dynamic information according to the second embodiment of the present invention.

  This process is executed in the work analysis server 112 as in the first embodiment. Further, analysis information obtained as a result of the statistical processing is accumulated in the worker information accumulation database 113.

  As in the first embodiment, the CPU 144 of the work analysis server 112 organizes the manufacturing activity information stored in the work performance accumulation database 111 for each worker (step 201), and for each worker and each work type. The required work time is extracted (step 202).

  The CPU 144 of the work analysis server 112 first acquires the minimum value A of the work required time from all the actual values (step 301), and sets it as the work required time with the highest efficiency in effect for the worker. Note that the minimum value A may not be the minimum value in the performance of the worker but the minimum value in the performance of all workers.

  The CPU 144 of the work analysis server 112 further divides a set of all actual values (work required time) for each work type of each worker at a predetermined sampling interval (for example, 1 hour). And the ratio B in which the performance value of the vicinity of the lowest value A is included among the performance value groups contained in the divided | segmented set is calculated (step 302). The vicinity of the lowest value A is, for example, a set of actual values within a certain range from the lowest value A. The reason why the predetermined range is provided is that work is performed by humans, and thus the time required for the work varies. Therefore, the range is set depending on the manufacturing site, specifically, a quantitative value range such as “minimum value A or more, minimum value A × 1.2 or less”, or “minimum value A or more, all actual values. A statistical value range of “below the average value of” is set.

  The ratio B can be considered as an index value for measuring the rate at which work with an efficiency close to the maximum efficiency is performed, that is, an index value for measuring the work stability of each worker. The calculated value is stored in the worker information storage database 113 as analysis information of manufacturing movement information. Here, the analysis information of the production dynamic information will be described with reference to FIG.

  FIG. 6 is a diagram illustrating an example of analysis information on manufacturing activity information accumulated in the worker information accumulation database 113 according to the second embodiment of this invention.

  The analysis information of the manufacturing activity information includes an operator ID 501, a process ID 502, a product type 503, statistical processing sampling section information 504, and a statistical value 505, as in the first embodiment. The worker ID 501, the process ID 502, the product type 503, and the statistical processing sampling section information 504 are the same as those in the first embodiment.

  The statistical value 505 includes a definition value 614 near the lowest value and a data number ratio 615 near the lowest value. The definition value 614 in the vicinity of the lowest value is a value set based on the lowest value A acquired from all the actual values. The production dynamic information having the work required time from the lowest value A to the defined value 614 near the lowest value corresponds to the work performed with the highest efficiency. The data number ratio 615 near the minimum value is the ratio of work performed at the highest efficiency, and the ratio B described above corresponds to this.

  Here, the description returns to the manufacturing dynamic information analysis process.

  As in the first embodiment, the work stability (ratio B) is also considered to vary depending on the worker's fatigue and other conditions. That is, when the ratio B tends to decrease, it can be determined that the worker's condition has deteriorated.

  The CPU 144 of the work performance collection server 110 detects a change in the ratio B (step 303) and determines whether the ratio B at a predetermined sampling interval has decreased (step 304). At this time, if the ratio B has not decreased (the result of step 304 is “No”), the calculation and monitoring of the ratio B is continued (step 305).

  When the ratio B decreases (the result of step 304 is “Yes”), the CPU 144 of the work record collection server 110 can determine that the work efficiency of the worker is decreasing (step 306). Then, a countermeasure such as notifying the worker himself / herself, the work supervisor, or the site manager that the work efficiency is reduced is implemented (step 309). The notification may be displayed on the local terminal 117 together with the work instruction, for example.

  As a countermeasure, the CPU 144 of the work result collection server 110 notifies the work plan management server 114 to reduce the work load of the worker according to the degree of reduction in work efficiency, and the work plan management server 114. May adjust the work plan. In this case, in the same manner as in the first embodiment, the manufacturing site is configured so that the load on the worker is reduced (for example, the work amount of the worker is reduced) by correcting or rescheduling the work plan. The work load as a whole is adjusted, and instructions are given to prevent a reduction in work efficiency as a whole manufacturing site.

  Furthermore, when a decrease in the rate B is detected not only once but continuously a plurality of times, or when a decrease amount equal to or greater than a predetermined threshold is detected, it is determined that the worker's condition has deteriorated, and a countermeasure is taken. You may make it implement.

  Further, as described above, by calculating the ratio B using the actual value set between the current time and the past one hour as the original data, it may be possible to observe a change in numerical values with higher real-time characteristics. Further, as described above, the history of the ratio B may be accumulated in association with conditions such as time zones, days of the week / dates, and environmental conditions such as weather, and the trend of the ratio B may be analyzed for each condition. . In addition, as described above, a decrease in work efficiency in a specific process or facility is detected without depending on the worker by calculating a ratio B for each fixed time for each process or for each facility used in the work. Thus, it becomes possible to detect deterioration of the process or equipment.

  According to the second embodiment of the present invention, as in the first embodiment, various factors such as optimization of work efficiency can be obtained by quantitatively indexing the degree of fatigue of each worker. Depending on the purpose, the work plan can be drawn up and adjusted in consideration of the condition or characteristics of the worker.

It is a figure which shows an example of the production management system of the 1st Embodiment of this invention. It is a flowchart which shows the procedure which analyzes the manufacturing dynamics information of the 1st Embodiment of this invention. It is a figure explaining an example of a structure of manufacturing dynamics information stored in the work performance accumulation | storage database of the 1st Embodiment of this invention. It is a figure which shows an example of the analysis information of the manufacture dynamics information accumulate | stored in the worker information storage database of the 1st Embodiment of this invention. It is a flowchart which shows the procedure which analyzes the manufacturing dynamics information of the 2nd Embodiment of this invention. It is a figure which shows an example of the analysis information of the manufacture dynamics information accumulate | stored in the worker information storage database of the 2nd Embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 101 Worker tag 102 Personal identification information 103 Article tag 104 Individual identification information 105 Local terminal 106 Tag reading process part 107 Work identification information 108 Work result transmission process part 109 Clock 110 Work result collection server 111 Work result accumulation database 112 Work analysis server 113 Worker information storage database 114 Work plan management server 115 Work plan planning processing unit 116 Work plan adjustment processing unit 117 Local terminal 118 Work instruction reception processing unit 119 Work instruction presentation processing unit 161 Work result reception processing unit 162 Work result storage processing unit 163 Statistical processing department

Claims (14)

A work analysis device that analyzes work results recorded for each work on an article at a manufacturing site,
A processor that executes processing for analyzing the work results, and a storage unit connected to the processor;
Each work result includes an identifier of the worker who performed the work, a work type indicating the type of the work, a work start time, and a work required time.
The processor is
Extract work results that match the worker and the work type,
For the extracted work results, for each predetermined period, calculate a statistical value of the work required time,
A work analysis apparatus for evaluating work efficiency of the work for each worker based on a statistical value calculated for each predetermined period.   The work analysis apparatus according to claim 1, wherein the statistical value is a variance of work required time included in work performance in the predetermined period.   The processor determines that the work efficiency of the work is lowered when the variance of the work required time included in the work results in the predetermined period increases with time. The work analysis device described in 1. The processor is
Acquire the shortest work required time in the extracted work results,
Based on the acquired shortest work required time, set a threshold value for determining whether the work efficiency of the work has decreased,
The work analysis apparatus according to claim 1, wherein the statistical value is a ratio in which work having a work required time shorter than the set threshold is included in work in the predetermined period. A production management method in a production management system comprising: a terminal installed for each manufacturing site; and a work analysis server that analyzes work results collected via the terminal,
Each work result includes an identifier of the worker who performed the work, a work type indicating the type of the work, a work start time, and a work required time.
The method
Collecting work records recorded for each work on the article at the manufacturing site;
From the collected work results, extract work results that match the worker and the work type,
For the extracted work results, for each predetermined period, calculate a statistical value of the work required time,
A production management method for evaluating work efficiency of the work for each of the workers based on a statistical value calculated for each predetermined period.   The production management method according to claim 5, wherein the statistical value is a variance of work required time included in work performance in the predetermined period.   The process for evaluating the work efficiency of the work is to determine that the work efficiency of the work is lowered when the variance of the work required time included in the work performance in the predetermined period increases with time. The production management method according to claim 6. The process of calculating the statistical value of the work required time is as follows:
Acquire the shortest work required time in the extracted work results,
Based on the acquired shortest work required time, set a threshold value for determining whether the work efficiency of the work has decreased,
The production management method according to claim 5, wherein a ratio of a work having a work required time shorter than the set threshold is included as the statistical value in the work in the predetermined period. The production management system further includes a work plan management server that manages a work plan for instructing work to the manufacturing site,
6. The production management method according to claim 5, wherein the work plan management server further creates the work plan based on a statistical value calculated for each of the predetermined periods. A production management system comprising a terminal installed for each manufacturing site, and a work analysis server that analyzes work results collected via the terminal,
Each work result includes an identifier of the worker who performed the work, a work type indicating the type of the work, a work start time, and a work required time.
The terminal obtains the work record recorded for each work on the article at the manufacturing site,
The work analysis server
Collect work results acquired on the terminal,
From the collected work results, extract work results that match the worker and the work type,
For the extracted work results, for each predetermined period, calculate a statistical value of the work required time,
A production management system for evaluating work efficiency of the work for each of the workers based on a statistical value calculated for each predetermined period.   The production management system according to claim 10, wherein the statistical value is a variance of work required time included in work performance in the predetermined period.   The work analysis server determines that the work efficiency of the work is lowered when the variance of the work required time included in the work results in the predetermined period increases with time. Item 12. The production management system according to Item 11. The work analysis server
Acquire the shortest work required time in the extracted work results,
Based on the acquired shortest work required time, set a threshold value for determining whether the work efficiency of the work has decreased,
11. The production management system according to claim 10, wherein a ratio of a work having a work required time shorter than the set threshold is included in the work in the predetermined period as the statistical value. The production management system further includes a work plan management server that manages a work plan for instructing work to the manufacturing site,
The work analysis server records a statistical value calculated for each predetermined period,
The production management system according to claim 10, wherein the work plan management server creates the work plan based on the recorded statistical values.

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