Man-hour management device, man-hour management method, and program

Abstract

To provide a man-hour management device, a man-hour management method and a program with which it is possible to obtain a planned man-hour that suits the actual work state that depends on a worker's characteristic.SOLUTION: The man-hour management device comprises: a man-hour information acquisition unit 111 for acquiring the operations content and of planning operations and a planned man-hour, worker information that includes the characteristic of a worker, and the operations content of actual operations and an actual man-hour; a storage unit 120 for classifying by worker characteristic pertaining to the actual man-hour and storing the actual man-hour acquired by the man-hour information acquisition unit 111; and an optimum planned man-hour calculation unit 112 for calculating an optimum planned man-hour on the basis of the characteristic and the actual man-hour, when the actual man-hour for the characteristic and the operations content stored in the storage unit 120 drops to or below the planned man-hour for the characteristic and the operations content. The man-hour information acquisition 111 updates the planned man-hour pertaining to the characteristic and the operations content to the optimum planned man-hour for the characteristic and the operations content that was calculated by the optimum planned man-hour calculation unit 112.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a man-hour management device, a man-hour management method, and a program. [Background technology]

[0002] At production sites including factories, systems that record work results are used for the purpose of managing work hours or improving work efficiency (for example, Patent Document 1). The work result recording system described in Patent Document 1 includes a voice recognition unit that recognizes input voice, and records a work result log including the start time of the work, the completion time of the work, and keywords recognized from the start time of the work to the completion time, based on the voice recognized by the voice recognition unit.

[0003] The work performance recording system described in Patent Document 1 is explained as improving work efficiency by allowing workers to input limited keywords by voice rather than inputting data manually, thereby eliminating the need for workers to interrupt their work to input work performance. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2017-167391 A Summary of the Invention [Problem to be solved by the invention]

[0005] The work performance recording system described in Patent Document 1 is intended for work at production sites, including factories. However, there is a demand not only in the production department, but also in other departments, including management departments and indirect departments, for recording work performance and improving work based on the results.

[0006] In order to improve work, it is necessary to compare the planned man-hours, which are the planned work time, with the actual man-hours, which are the time that the worker actually took to perform the work, and extract problems or areas for improvement in the work based on the difference between the planned man-hours and the actual man-hours. However, there is a problem that the planned man-hours do not match the actual work situation, making it difficult to evaluate correctly.

[0007] The present disclosure has been made in consideration of the above-mentioned circumstances, and aims to provide a man-hour management device, a man-hour management method, and a program that can obtain planned man-hours that correspond to the actual work status based on the characteristics of workers. [Means for solving the problem]

[0008] In order to achieve the above object, the man-hour management device of the present disclosure includes a man-hour information acquisition unit that acquires planned man-hours, which are the work contents of planned tasks and the time required for each of the work contents, worker information including the characteristics of the worker, and actual man-hours, which are the work contents of actual tasks that are tasks performed by the worker and the time required for each of the work contents. The planned man-hours acquired by the man-hour information acquisition unit are classified and stored according to the characteristics of the workers and the contents of the work, and A storage unit that classifies and stores the actual man-hours acquired by the man-hour information acquisition unit according to the characteristics and work contents of the workers related to the actual man-hours, and when the actual man-hours for the characteristics and work contents stored in the storage unit become equal to or less than the planned man-hours for the characteristics and work contents, A trained model is generated by machine learning using learning data including the actual labor hours, and the trained model is used to calculate the performance of the work when the characteristics of the worker are input. and an optimal planned man-hour calculation unit that calculates an optimal planned man-hour. The man-hour information acquisition unit updates the planned man-hours related to the characteristics and the work content to the optimal planned man-hours for the characteristics and the work content calculated by the optimal planned man-hour calculation unit. Effect of the Invention

[0009] According to the present disclosure, the planned man-hours are updated based on the actual man-hours classified by the characteristics of the workers, so that it is possible to obtain the planned man-hours that correspond to the actual work conditions based on the characteristics of the workers. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing a configuration example of a man-hour management device according to an embodiment of the present disclosure. [Diagram 2] Flowchart of man-hour information acquisition process [Diagram 3] Flowchart of optimal planned man-hour calculation process [Figure 4] (a) is a block diagram showing a learning device of the optimal planned man-hour calculation unit, and (b) is a flowchart of the learning process. [Diagram 5] (a) is a block diagram showing an inference device of the optimal planned man-hour calculation unit, and (b) is a flowchart of the inference process. [Figure 6] Table showing worker characteristics and actual man-hours [Figure 7] (a) A table showing data on planned work (b) A table showing data on actual work (c) A time chart for planned work (d) A time chart for actual work [Figure 8] Table showing points of focus and proposals for work improvement DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] (Embodiment) Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0012] FIG. 1 is a block diagram showing an example of the configuration of a man-hour management device 1 according to an embodiment of the present disclosure. The man-hour management device 1 is a device that records planned work, planned man-hours, actual work, and actual man-hours, and outputs improvement proposals for work. The planned work and actual work include a wide variety of work contents in any department including the production department, management department, or indirect department. In this embodiment, a case will be described in which the work contents of the planned work and actual work are work contents in an indirect department including document creation and data analysis.

[0013] The labor-hour management device 1 includes a calculation processing unit 110 that acquires labor-hour information, optimizes planned labor hours, and executes processing to generate improvement proposals for actual work, a memory unit 120 that stores data acquired or generated by the calculation processing unit 110, an operation input unit 130 that accepts operation input from an operator, and a display unit 140 that displays information acquired or generated by the calculation processing unit 110.

[0014] The calculation processing unit 110 is an arbitrary calculation processing device, for example, a CPU (Central Processing Unit). The calculation processing unit 110 executes a program stored in the storage unit 120 to function as a man-hour information acquisition unit 111, an optimal planned man-hour calculation unit 112, a planned / actual man-hour difference extraction unit 113, an improvement focus extraction unit 114, and an improvement proposal output unit 115.

[0015] The man-hour information acquisition unit 111 of the calculation processing unit 110 acquires worker information including the characteristics of the workers, and man-hour information including the planned man-hours and the actual man-hours, and stores them in the storage unit 120. The optimal planned man-hour calculation unit 112 calculates the optimal planned man-hours by artificial intelligence (AI) based on the characteristics of the workers and the actual man-hours.

[0016] The planned / actual man-hour difference extraction unit 113 extracts the difference between the planned man-hours and the actual man-hours, and the improvement viewpoint extraction unit 114 extracts an improvement viewpoint based on the difference between the planned man-hours and the actual man-hours. The improvement proposal output unit 115 obtains and outputs an improvement proposal for the business from the storage unit 120 based on the extracted improvement viewpoint.

[0017] The storage unit 120 is any storage device, for example, a non-volatile semiconductor memory including a flash memory or an erasable programmable read only memory (EPROM), or a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a digital versatile disk (DVD).

[0018] 1, the storage unit 120 includes a planned / actual man-hours database 121 that stores data including planned man-hours and actual man-hours, a trained model storage unit 122 that stores trained models used by the optimal planned man-hours calculation unit 112, and an improvement plan database 123 that stores data on improvement plans for business operations. The storage unit 120 further stores a program executed by the calculation processing unit 110.

[0019] The following is a detailed description of the functions of the arithmetic processing unit 110 and the contents of the storage unit 120. First, the operation of the man-hour information acquisition unit 111 of the arithmetic processing unit 110 will be described with reference to the flowchart of Fig. 2. Fig. 2 is a flowchart of the man-hour information acquisition process.

[0020] The man-hour information acquisition unit 111 uses a scheduler and a work performance recording tool to acquire worker information including worker characteristics, planned tasks, planned man-hours, actual tasks, and actual man-hours based on information input by an operator to the operation input unit 130. Here, the operator is a person permitted to operate the man-hour management device 1, and is, for example, a worker who performs tasks, or a manager who manages the tasks and man-hour management device 1. Moreover, the worker characteristics are qualities that affect the worker's work, such as age, job title, years of experience, qualifications, and skills.

[0021] The scheduler is any software with a schedule management function, and may be a general-purpose schedule management software. The scheduler is software that can write out data on planned tasks and planned man-hours that can be read by a work performance recording tool. For example, a worker uses the scheduler to input the task content of the day's planned tasks and the scheduled start time and scheduled completion time of each task at the start of each day or for each predetermined period.

[0022] Here, since the names of tasks may vary depending on the operator or region, the task contents are listed in advance and the operator can select from them. This makes it possible to avoid tasks being counted as different tasks when counting man-hour data, even though they are the same task, due to differences in the names of the tasks.

[0023] The work performance recording tool is a tool that has the function of importing data from a scheduler and collecting data related to actual work, and is a tool created, for example, using the macro function of EXCEL (registered trademark) VBA or other general-purpose spreadsheet software.

[0024] The work performance recording tool takes in the work content of the planned work and the scheduled start time and scheduled completion time of each work content from the scheduler, and also acquires the work content of the performed work and the start time and completion time of each work content based on the operator's input to the operation input unit 130. For example, the work performance recording tool has an input form that allows the operator to input the work content and to press or click a button at the start and completion timing of each work content.

[0025] The work record recording tool has the same list of work contents as that used by the scheduler, and the operator selects the work contents of the performed work from the list of work contents. In addition, the work record recording tool records the start time and completion time of the selected work contents when the operator presses a button and clicks the button at the timing of starting and completing the work.

[0026] 2, first, the man-hour information acquisition unit 111 acquires the work content, the scheduled start time, and the scheduled completion time of the planned work from the scheduler (step S101). Next, the man-hour information acquisition unit 111 imports the work content, the scheduled start time, and the scheduled completion time of the planned work acquired from the scheduler into the work performance recording tool (step S102).

[0027] Next, the man-hour information acquisition unit 111 acquires the start time and completion time of each task content of the actual work based on the information input by the operator using the work performance recording tool (step S103). The man-hour information acquisition unit 111 calculates the planned man-hours, which are the time required for each task content, from the scheduled start time and scheduled completion time of each task content of the planned work acquired using the work performance recording tool. The man-hour information acquisition unit 111 also calculates the actual man-hours, which are the time required for each task content, from the start time and completion time of each task content of the actual work acquired using the work performance recording tool (step S104).

[0028] Here, the planned man-hours or actual man-hours are the time required for the relevant business content, so if other business tasks or breaks are performed on the way, the time for the other tasks or breaks is subtracted from the time from the scheduled start time to the scheduled completion time or from the time from the start time to the completion time. In step S104, in order to facilitate prioritizing business improvements or converting the effects of business improvements into a monetary value, the man-hour information acquisition unit 111 may use a work performance recording tool to multiply the actual man-hours by the worker's hourly processing cost rate to calculate and visualize the actual input amount.

[0029] Thereafter, the man-hour information acquisition unit 111 classifies the planned man-hours by the characteristics of the worker and the work content (step S105) and stores them in the planned and actual man-hours database 121 of the storage unit 120. The man-hour information acquisition unit 111 also classifies the actual man-hours by the characteristics of the worker and the work content (step S105) and stores them in the planned and actual man-hours database 121 of the storage unit 120 (storing step). When the actual input amount is calculated in step S104 by multiplying the hourly processing cost rate of the worker, the actual input amount is stored in the planned and actual man-hours database 121 together with the actual man-hours.

[0030] Through the above-described man-hour information acquisition process, the planned man-hours and actual man-hours are stored in the planned / actual man-hours database 121 of the storage unit 120, grouped by the task content and the characteristics of the worker. For example, the planned man-hours and actual man-hours for the task content of creating document A are stored in groups by the worker's age, job title, years of experience, qualifications, and skills.

[0031] The optimal planned man-hour calculation unit 112 of the calculation processing unit 110 uses artificial intelligence (AI) to calculate the optimal planned man-hours based on the data stored in the planned / actual man-hours database 121. The operation of the optimal planned man-hour calculation unit 112 of the calculation processing unit 110 will be described with reference to the flowchart of Fig. 3. Fig. 3 is a flowchart of the optimal planned man-hours calculation process.

[0032] First, the optimal planned man-hour calculation unit 112 acquires the actual man-hours stored in the planned / actual man-hours database 121 (step S201). Next, the acquired actual man-hours are compared with the planned man-hours classified into the same work content and the same worker characteristics (step S202).

[0033] If the actual man-hours acquired in step S201 are shorter than the planned man-hours classified into the same work content and the same worker characteristics (step S203: Yes), the data of the actual man-hours is added to the learning data together with the data of the work content and the worker characteristics (step S204).If the actual man-hours acquired in step S201 are equal to or greater than the planned man-hours classified into the same work content and the same worker characteristics (step S203: No), the process proceeds to step S205.

[0034] The optimal planned man-hour calculation unit 112 calculates the optimal planned man-hour by AI using learning data including the actual man-hours, the work content, and the characteristics of the workers (step S205: optimal planned man-hour calculation step). The calculation of the optimal planned man-hours in step S205 may be performed every time the actual man-hours are added to the learning data in step S204, or may be performed when a predetermined amount of learning data is accumulated. Alternatively, it may be performed at any predetermined time interval.

[0035] 1, the AI ​​that calculates the optimal planned man-hours is realized by an optimal planned man-hours calculation unit 112 of the arithmetic processing unit 110 and a trained model storage unit 122 of the storage unit 120. The optimal planned man-hours calculation unit 112 includes a learning device 217 and an inference device 218 (see FIGS. 4(a) and 5(a)).

[0036] In this embodiment, the learning device 217 and the inference device 218 are included in the man-hour management device 1. The learning device 217 and the inference device 218 are used to learn the optimal planned man-hours for a target work content. The target work content is any work content carried out in any department including the production department, the management department, and the indirect department, for example, a productivity analysis material preparation job or a monthly report material preparation job in an indirect department.

[0037] The learning device 217 and the inference device 218 may take any form, and may be, for example, part of the man-hour management device 1, or may exist on a server externally connected to the man-hour management device 1 via a communication network.

[0038] First, the learning process will be described. Fig. 4(a) is a block diagram showing the learning device 217 of the optimal planned man-hour calculation unit 112, and Fig. 4(b) is a flowchart of the learning process. Here, learning will be described when the work content is a document creation work.

[0039] 4(a), the learning device 217 includes a data acquisition unit 2171 and a model generation unit 2172. The data acquisition unit 2171 acquires the actual man-hours of the document creation work and the characteristics of the workers related to the actual man-hours, which are added to the learning data in step S204 of the optimal planned man-hour calculation process shown in FIG.

[0040] The model generation unit 2172 learns based on the learning data including information on the combination of new actual man-hours and worker characteristics acquired by the data acquisition unit 2171. That is, a trained model for inferring the optimal planned man-hours from the new actual man-hours and worker characteristics required for the target document creation task is generated. The learning data in this learning process is data in which the new actual man-hours and worker characteristics are associated with each other.

[0041] The learning algorithm used by the model generation unit 2172 may be a conventional algorithm, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0042] In addition, deep learning may be performed to learn how to extract the features themselves, or machine learning may be performed according to genetic programming, functional logic programming, or support vector machines.

[0043] In this embodiment, a case where a neural network is applied will be described as an example. In this case, the model generation unit 2172 learns the optimal planned man-hours for the characteristics of the workers by supervised learning according to the neural network model. Here, supervised learning refers to a method of providing a learning device with a set of input and result (label) data, learning the features of the learning data, and inferring the result from the input.

[0044] A neural network is composed of an input layer having multiple neurons, an intermediate layer (hidden layer) having multiple neurons, and an output layer having multiple neurons. The intermediate layer may be one layer or two or more layers.

[0045] In the neural network of this embodiment, a model generation unit 2172 learns optimal planned man-hours for the characteristics of a worker by supervised learning, based on learning data including information on combinations of new actual man-hours and characteristics of a worker acquired by a data acquisition unit 2171.

[0046] That is, the model generation unit 2172 adjusts the weight W1 between the input layer and the middle layer and the weight W2 between the middle layer and the output layer for each characteristic of the worker, and learns by determining the weights W1 and W2 that can bring the output from the output layer when the characteristic of a new worker is input to the input layer closer to the actual labor hours, which is the result (correct answer). For example, when the characteristics of a worker who performed a document creation task, including the age and years of experience, are input to the input layer, an index indicating the amount of waste in the action is obtained in the middle layer, and the optimal planned labor hours are output to the output layer, which is compared with the actual labor hours, which is the correct answer, to determine the weights W1 and W2.

[0047] The trained model storage unit 122 stores the trained model output by the model generation unit 2172.

[0048] The flow of the learning process is as shown in the flowchart of Fig. 4(b). First, the data acquisition unit 2171 of the learning device 217 acquires data on new actual man-hours and worker characteristics (step S301). Here, the new actual man-hours and worker characteristics are acquired simultaneously, but it is sufficient if the new actual man-hours and worker characteristics are acquired in association with each other, and the data acquisition unit 2171 may acquire the data on the new actual man-hours and worker characteristics at different times.

[0049] Thereafter, the model generation unit 2172 performs a learning process based on the combination of the new actual man-hours and the characteristics of the workers acquired by the data acquisition unit 2171 (step S302) to generate a trained model. Then, the generated trained model is stored in the trained model storage unit 122 (step S303).

[0050] Next, the inference process will be described below. Fig. 5(a) is a block diagram showing the inference device 218 of the optimal planned man-hour calculation unit 112, and Fig. 5(b) is a flowchart of the inference process.

[0051] Here, a case where an optimal planned man-hours for a document creation task is inferred will be described. As shown in Fig. 5(a), the inference device 218 includes a data acquisition unit 2181 and an inference unit 2182. The data acquisition unit 2181 acquires the characteristics of a new worker acquired by the man-hour information acquisition unit 111 as input data.

[0052] The inference unit 2182 infers the optimal planned man-hours for the characteristics of the worker acquired by the data acquisition unit 2181, using the learned model stored in the learned model storage unit 122. That is, the inference unit 2182 inputs the characteristics of the worker into the learned model, and outputs the optimal planned man-hours inferred from the characteristics of a new worker.

[0053] In this embodiment, a configuration has been described in which the optimal planned man-hours are output using a trained model generated by the model generation unit 2172 of the learning device 217. However, it is also possible to obtain a trained model from outside the man-hour management device 1 and infer the optimal planned man-hours based on this trained model.

[0054] The flow of the inference process is as shown in the flowchart of Fig. 5(b). First, the data acquisition unit 2181 of the inference device 218 acquires data on the characteristics of a new worker (step S401). Next, the inference unit 2182 inputs the characteristics of the new worker into the trained model stored in the trained model storage unit 122 (step S402), and infers and outputs the optimal planned man-hours (step S403).

[0055] The optimal planned man-hour calculation unit 112 updates the planned man-hours stored in the planned / actual man-hour database 121 to the optimal planned man-hours calculated in step S403 (step S404: update step). At this time, the characteristics of the workers related to the planned man-hours before and after the update are the same.

[0056] In this way, the planned man-hours that are optimal for the characteristics of the worker can be updated, and the currently optimal planned man-hours can be used for man-hour management when a worker with the same characteristics works later. In other words, by utilizing man-hour data classified by the characteristics of the worker, including age, job title, years of experience, qualifications, and skills, for man-hour management when a worker performs a task that he has no experience with, or when the worker is a newcomer and it is difficult to roughly estimate the planned man-hours, it becomes possible to set a schedule that matches the ability or capability of the worker.

[0057] In addition, since the new planned man-hours are values ​​calculated from the actual man-hours up to now, when a worker is performing a task that he or she has no experience with or when the worker is a new employee, the worker can improve the processing speed by being aware of this value. In addition, for repeat tasks that are performed repeatedly, applying the new planned man-hours makes it possible to understand that the previous planned man-hours are not optimal, and to bring the actual man-hours closer to the optimal planned man-hours. In cases where the planned man-hours used up to now are long, revising them to the optimal planned man-hours increases the amount of planned work that can be processed in a day, making it possible to improve worker productivity.

[0058] The planned / actual man-hour difference extraction unit 113 compares the actual man-hours acquired by the man-hour information acquisition unit 111 with the planned man-hours calculated by the optimal planned man-hour calculation unit 112, and extracts the difference in man-hours. For example, the work result recording tool of the man-hour information acquisition unit 111 acquires the actual tasks and actual man-hours from the scheduler and graphs them for each worker, and the planned man-hours and planned tasks calculated by the optimal planned man-hour calculation unit 112 are graphed, and the difference is visualized. Any method of visualization may be used, and for example, a time chart or Gantt chart may be automatically created using the work result recording tool and presented to the worker.

[0059] The improvement viewpoint extraction unit 114 selects the work content for which the difference between the planned man-hours and the actual man-hours extracted by the planned-actual man-hour difference extraction unit 113 is equal to or greater than a predetermined threshold, and extracts an improvement viewpoint for the work content. The improvement viewpoint may be selected from a predetermined category. For example, improvement viewpoints related to the seven wastes that lie in the indirect department described in a non-patent document (Hirano Hiroyuki, Nikkan Kogyo Shimbun, "Visually Understandable Indirect Just-in-Time"), namely, waste of doing too much, waste of stagnation, waste of movement, waste of mistakes, waste of work itself, waste of movement, and waste of waiting, may be stored in association with the work content, and an improvement viewpoint may be extracted based on the work content of the actual work. Alternatively, the improvement viewpoint may be extracted from information input by the worker based on the past work performance, which is accumulated.

[0060] Improvement plans are classified and stored in the improvement plan database 123 of the storage unit 120 according to the business content and the improvement viewpoint. For example, for the business content of document creation, the improvement plan database 123 stores improvement plans associated with the improvement viewpoints related to the seven wastes that lurk in the indirect department, namely, waste of overdoing, waste of stagnation, waste of movement, waste of mistakes, waste of the work itself, waste of motion, and waste of waiting.

[0061] The improvement proposal output unit 115 extracts improvement proposals stored in the improvement proposal database 123 based on the improvement viewpoints extracted by the improvement viewpoint extraction unit 114 and information on actual work and actual labor hours, and displays the list on the display unit 140.

[0062] The operation of the man-hour management device 1 configured as above will be described with reference to the examples shown in FIGS.

[0063] The man-hour information acquisition unit 111 acquires worker information including worker characteristics, the work contents of the planned work and the actual work, and the planned man-hours and the actual man-hours based on information input by an operator to the operation input unit 130 using a scheduler and a work performance recording tool, and stores them in a planned / actual man-hour database 121 in the storage unit 120. In the planned / actual man-hour database 121, the planned man-hours and the actual man-hours are stored, grouped according to the work contents and the characteristics of the worker.

[0064] Fig. 6 shows an example of actual man-hours stored in the planned and actual man-hours database 121. Here, it shows actual man-hours for the task of creating document A, which is performed in an indirect department, and is classified by the worker's characteristics, such as age, job title, and years of experience.

[0065] The optimal planned man-hour calculation unit 112 uses AI to calculate the optimal planned man-hours based on the data stored in the planned / actual man-hour database 121. Specifically, the optimal planned man-hour calculation unit 112 generates a trained model based on the actual man-hours classified by the work content and worker characteristics stored in the planned / actual man-hour database 121, and calculates the optimal planned man-hours for the work content and worker characteristics using the trained model.

[0066] The planned man-hours in the planned / actual man-hours database 121 are updated with the optimal planned man-hours calculated by the optimal planned man-hours calculation unit 112, and are used as the planned man-hours for the tasks in the subsequent work. The planned / actual man-hours difference extraction unit 113 compares the planned man-hours to which the optimal planned man-hours calculated by the optimal planned man-hours calculation unit 112 are applied with the newly acquired actual man-hours, and outputs the comparison result.

[0067] Figure 7 is a diagram showing examples of data on the work content and time of planned work and actual work, where (a) is data on the work content and time of planned work, (b) is data on the work content and time of actual work, (c) is a time chart of planned work, and (d) is a time chart of actual work.

[0068] The planned / actual man-hour difference extraction unit 113 acquires the planned man-hours and the actual man-hours calculated based on the data of the work contents and time of the planned work and the actual work, and extracts the work with a large difference between the planned man-hours and the actual man-hours. In addition, the planned / actual man-hour difference extraction unit 113 can visualize the difference between the planned man-hours and the actual man-hours by displaying it in any form including a time chart or a Gantt chart.

[0069] The improvement viewpoint extraction unit 114 selects the work contents for which the difference between the planned man-hours and the actual man-hours extracted by the planned / actual man-hour difference extraction unit 113 is equal to or greater than a predetermined threshold, and extracts improvement viewpoints for the work contents of the actual work. Figure 8 shows the extraction of improvement viewpoints for the seven wastes lurking in the indirect departments, namely, waste of doing too much, waste of stagnation, waste of movement, waste of mistakes, waste of the work itself, waste of action, and waste of waiting. For example, improvement viewpoints may be extracted from information input by workers based on the results of past improvement measures, which is accumulated.

[0070] Improvement measures implemented in the past are classified by work content and viewpoint and stored as improvement plans in the improvement plan database 123 of the storage unit 120. For example, for the work of creating documents, improvement plans associated with improvement viewpoints related to the seven wastes lurking in indirect departments, namely, waste of doing too much, waste of stagnation, waste of movement, waste of mistakes, waste of the work itself, waste of motion, and waste of waiting, are stored in the improvement plan database 123.

[0071] The improvement proposal output unit 115 extracts improvement proposals stored in the improvement proposal database 123 based on the information on the improvement viewpoints and business contents extracted by the improvement viewpoint extraction unit 114, and displays the list on the display unit 140. Fig. 8 shows an example of the display of the improvement viewpoints and improvement proposals.

[0072] By having the worker recognize the improvement plan obtained in this way and carry out the subsequent work, the worker's productivity can be improved.

[0073] As described above, in the man-hour management device 1 according to the present embodiment, the man-hour information acquisition unit 111 acquires the task content and planned man-hours of the planned task, the worker information including the characteristics of the worker, and the task content and actual man-hours of the actual task performed by the worker, and classifies the acquired actual man-hours according to the characteristics and task content, and stores them in the planned and actual man-hours database 121 of the storage unit 120. The optimal planned man-hour calculation unit 112 calculates the optimal planned man-hours based on the characteristics and the actual man-hours when the actual man-hours for the worker characteristics and task content stored in the planned and actual man-hours database 121 become equal to or less than the planned man-hours for the characteristics and task content. Then, the man-hour information acquisition unit 111 updates the planned man-hours for the characteristics and task content to the optimal planned man-hours for the characteristics and task content calculated by the optimal planned man-hour calculation unit 112. As a result, the optimal planned man-hours are updated based on the actual man-hours that are equal to or less than the planned man-hours, so that it is possible to use the planned man-hours that are in line with the actual work state due to the characteristics of the worker.

[0074] In the above embodiment, the planned / actual man-hour database 121, the learned model memory unit 122, and the improvement plan database 123 are included in the memory unit 120 of the man-hour management device 1, but they may also be stored in an external storage device communicatively connected to the man-hour management device 1.

[0075] In addition, in the optimal planned man-hour calculation process shown in FIG. 3, when the actual man-hour is shorter than the planned man-hour, the actual man-hour is used as learning data for learning. However, when there are multiple actual man-hours that are shorter than the planned man-hour within a predetermined period, only the shortest actual man-hour may be used as learning data.

[0076] In addition, in the above embodiment, the trained model generated in the optimal planned labor hours calculation process is a model generated for a task content selected from task contents that include document creation, and the optimal planned labor hours are inferred based on the actual labor hours related to the task content, but the trained model generated for the task content may also be used to infer the optimal planned labor hours related to related or similar task content.

[0077] The hardware configuration and the flowchart shown in the above embodiment are merely examples and can be modified or applied. The functions realized by the arithmetic processing unit 110 and the storage unit 120 can be realized by using a normal computer system, not a dedicated system.

[0078] For example, a computer capable of implementing each function may be configured by distributing a program for executing the operations of the above-described embodiments by storing it on a computer-readable recording medium such as a CD-ROM (Compact Disc Read-Only Memory), a DVD (Digital Versatile Disc), an MO (Magneto Optical Disc), or a memory card, and installing the program on a computer. In addition, when each function is implemented by sharing the work between an OS (Operating System) and an application, or by cooperation between the OS and an application, only the parts other than the OS may be stored on the recording medium.

[0079] The present disclosure is not limited to the above-described embodiment, and various modifications are possible without departing from the gist of the present disclosure. [Explanation of symbols]

[0080] 1 Man-hour management device, 110 arithmetic processing unit, 111 man-hour information acquisition unit, 112 optimal planned man-hour calculation unit, 113 planned / actual man-hour difference extraction unit, 114 improvement focus extraction unit, 115 improvement proposal output unit, 120 memory unit, 121 planned / actual man-hour database, 122 learned model memory unit, 123 improvement proposal database, 130 operation input unit, 140 display unit, 217 learning device, 218 inference device, 2171 data acquisition unit, 2172 model generation unit, 2181 data acquisition unit, 2182 inference unit.

Claims

1. a man-hour information acquisition unit that acquires planned man-hours, which are the work contents of planned work and the time required for each work content, worker information including the characteristics of the worker, and actual man-hours, which are the work contents of actual work, which are work performed by the worker, and the time required for each work content; a storage unit that classifies and stores the planned man-hours acquired by the man-hour information acquisition unit according to the characteristics of the workers and the work content, and that classifies and stores the actual man-hours acquired by the man-hour information acquisition unit according to the characteristics of the workers related to the actual man-hours and the work content; an optimal planned man-hour calculation unit that generates a trained model by machine learning using learning data including the characteristics and the actual man-hours when the actual man-hours for the characteristics and the work content stored in the storage unit become equal to or less than the planned man-hours for the characteristics and the work content, and calculates an optimal planned man-hours for the work content when the characteristics of the worker are input using the trained model; The man-hour information acquisition unit updates the planned man-hours related to the characteristics and the work content to the optimal planned man-hours calculated by the optimal planned man-hour calculation unit for the characteristics and the work content. Man-hours management device.

2. The characteristics of the worker include at least one of the worker's age, job title, years of work experience, qualifications, and skills; The man-hour management device according to claim 1 .

3. The actual man-hours are calculated from the start time and completion time of the work content input by the worker using a work performance recording tool that imports the work content and the planned man-hours of the planned work from schedule management software. The man-hour management device according to claim 1 or 2.

4. The optimal planned man-hour calculation unit is A learning device that generates the trained model by machine learning using the training data; and an inference device that infers the optimal planned man-hours for the work content when the characteristics of the worker are input, using the trained model generated by the learning device; having The man-hour management device according to any one of claims 1 to 3.

5. The storage unit further stores an improvement viewpoint associated with the business content and an improvement plan classified according to the business content and the improvement viewpoint, an improvement focus extraction unit that compares the actual man-hours for the characteristics and the work content of the worker with the planned man-hours for the characteristics and the work content, and extracts an improvement focus corresponding to the work content of the actual work stored in the storage unit when there is a difference equal to or greater than a predetermined threshold; and an improvement proposal output unit that outputs an improvement proposal associated with the business content and the improvement viewpoint of the performance business stored in the storage unit. The man-hour management device according to any one of claims 1 to 4.

6. The improvement focus point is a focus point related to at least one of the following: waste of doing too much, waste of stagnation, waste of movement, waste of mistakes, waste of the work itself, waste of movement, and waste of on-hand time. The man-hour management device according to claim 5 .

7. A computer-implemented labor-hour management method, comprising: A first storage step of classifying and storing the content of the planned work and the planned man-hours required for each content of the planned work according to the characteristics of the workers and the content of the work; a second storage step of classifying and storing the actual man-hours, which are the time required for each task content of the actual tasks performed by the worker, according to the characteristics of the worker and the task content; an optimal planned man-hour calculation step of generating a trained model by machine learning using learning data including the characteristics and the actual man-hours when the actual man-hours for the characteristics and the work content of the worker stored in the second storage step become equal to or less than the planned man-hours for the characteristics and the work content stored in the first storage step, and calculating an optimal planned man-hours for the work content when the characteristics of the worker are input using the trained model; An update step of updating the planned man-hours related to the characteristics and the work content to the optimal planned man-hours for the characteristics and the work content calculated in the optimal planned man-hours calculation step. Man-hour management methods.

8. Computer, a storage unit that classifies and stores planned man-hours, which are the work contents of planned work and the time required for each work content, by worker characteristics and the work content, and also classifies and stores actual man-hours, which are the time required for each work content of actual work performed by the worker, by worker characteristics and the work content; an optimal planned man-hour calculation unit that generates a trained model by machine learning using learning data including the characteristics and the actual man-hours of the worker stored in the storage unit when the actual man-hours for the characteristics and the work content become equal to or less than the planned man-hours for the characteristics and the work content, and calculates an optimal planned man-hours for the work content when the characteristics of the worker are input using the trained model; a man-hour information acquisition unit that acquires the optimal planned man-hours for the characteristic and the task content calculated by the optimal planned man-hour calculation unit as the planned man-hours related to the characteristic and the task content; A program that functions as a

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