Process plan creation support device, process plan creation support system, process plan creation support method, and program

Abstract

A process plan creation support device (1) is provided with: a personnel arrangement calculation unit (18) that calculates personnel arrangement for an element work corresponding to a production plan indicated by production plan information acquired by a production plan information acquisition unit (15), on the basis of attendant information, work definition information, and work time information for each worker; a process plan information generation unit (19) that exchanges the production order, causes the personnel arrangement calculation unit (18) to calculate the personnel arrangement, and generates process plan information indicating the production order and the personnel arrangement that maximizes an evaluation value for evaluating productivity; and a work time information updating unit (14) that updates work time information for workers who have work performance in the past on the basis of imaging information indicating images in which the workers have been imaged, and that updates work time information for workers who have work performance in the past on the basis of work time information for workers who have work performance in the past and have attributes most similar to those of workers who do not have work performance in the past. Element work time of workers who do not have work performance in the past is predicted, and work time information of workers who do not have work performance in the past is generated.

Description

Technical Field

[0001] The present invention relates to a process plan making auxiliary device, a process plan making auxiliary system, a process plan making auxiliary method and a program. Background Art

[0002] There is a method in which personnel are assigned to a production plan of a product at a manufacturing site, information on the working time of all operators is obtained while assigning working instructions to each operator, and efficient working instructions are assigned to the operators to maximize the evaluation value of productivity.

[0003] Patent Document 1 discloses a process planning support device that calculates the work time corresponding to the work body for the work assigned to each work station of an assembly line, and calculates and outputs an evaluation index for each work station based on the calculated work time.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2018-26071 Summary of the invention

[0007] Problems to be solved by the invention

[0008] In the technology described in Patent Document 1, when there are workers such as newcomers and supporters who do not know the working time due to lack of data, it is unknown how long it takes the workers to complete the work, and sometimes it is impossible to give efficient work instructions.

[0009] The present invention has been made to solve the above-mentioned problem, and an object of the present invention is to provide efficient work instructions even when there are workers who are not clear about the work time.

[0010] Means for solving problems

[0011] In order to achieve the above-mentioned purpose, the process plan making auxiliary device of the present invention has a production plan information acquisition unit, a staffing calculation unit, a process plan information generation unit and a work time information update unit. The production plan information acquisition unit acquires production plan information representing the production plan of the product. The staffing calculation unit calculates the staffing for the element work corresponding to the production plan shown in the production plan information based on the attendance information representing the operators who are on duty, i.e., the attendees, the job definition information representing the element work of each process constituting the production process of the product, and the work time information representing the time spent on the element work of each operator, i.e., the element work time. The process plan information generation unit changes the production sequence, so that the staffing calculation unit calculates the staffing, and generates process plan information representing the staffing and production sequence that maximizes the evaluation value for evaluating productivity. The operation time information updating unit updates the operation time information of the operator who had operation performance in the past based on the shooting information representing the image of the operator, predicts the element operation time of the operator who had no operation performance in the past based on the operation time information of the operator who had operation performance in the past whose attributes are most similar to the operator who had no operation performance in the past, and generates the operation time information of the operator who had no operation performance in the past.

[0012] Effects of the Invention

[0013] According to the present invention, when a process plan is prepared that maximizes the evaluation value of productivity based on the working time of each operator, the element working time of the operator with no working performance in the past is predicted based on the working time information of the operator with no working performance in the past whose attributes are most similar to those of the operator with no working performance in the past, and working time information representing the element working time of the operator with no working performance in the past is generated. As a result, even when there are operators who are unclear about their working time, efficient working instructions can be performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a diagram showing a configuration example of the process plan preparation support system according to the first embodiment.

[0015] Figure 2 This is a diagram showing a functional configuration example of the process plan preparation support device according to the first embodiment.

[0016] Figure 3 This is a diagram showing an example of work time information according to the first embodiment.

[0017] Figure 4 This is a diagram showing an example of job definition information according to the first embodiment.

[0018] Figure 5 This is a diagram showing a functional configuration example of a work time information updating unit according to the first embodiment.

[0019] Figure 6 This is a diagram showing an example of personal attribute information according to the first embodiment.

[0020] Figure 7 This is a diagram showing the character attribute information of the new person X in the first embodiment.

[0021] Figure 8 This is a diagram conceptually showing the process of predicting the element work time of the newcomer X in the first embodiment.

[0022] Fig. 9 This is a diagram showing an example of a graph of probability distribution of element work time of a newcomer X according to the first embodiment.

[0023] Fig.10 This is a flowchart showing an example of the operation of the process plan preparation support processing according to the first embodiment.

[0024] Fig.11 This is a flowchart showing an example of the operation of the work time prediction process according to the first embodiment.

[0025] Fig.12 This is a diagram showing an example of the hardware configuration of the process plan preparation support device according to the first and second embodiments. DETAILED DESCRIPTION

[0026] Hereinafter, the process plan making assisting device, process plan making assisting system, process plan making assisting method and program of the present embodiment will be described in detail with reference to the accompanying drawings. In addition, in the drawings, the same reference numerals are given to the same or corresponding parts. In the following embodiments, an example of assisting in making a process plan for allocating operations included in the process of producing a product to operators according to the production plan of the product will be described. The product is an example of a manufactured product.

[0027] (Implementation Method 1)

[0028] use Figure 1 The structure of the process plan preparation support system 100 of the first embodiment is described. The user of the process plan preparation support system 100 (the manager in the figure) inputs the production plan information indicating the production plan of the product of the day to the process plan preparation support device 1. The production plan information here is information indicating the number of units produced for each model of the product, and the order of production may not be determined. The model of the product is an example of the category of the product.

[0029] The camera 2 is a camera for photographing the worker. For example, a 3D camera capable of photographing the hand of the worker who is working is fixedly installed as the camera 2. The camera 2 transmits photographing information indicating the image of the worker to the process planning support device 1.

[0030] The process plan making auxiliary device 1 calculates the staffing for the operation corresponding to the production plan based on the input production plan information and the received shooting information. The process plan making auxiliary device 1 calculates the evaluation value for evaluating productivity based on the calculated staffing. The process plan making auxiliary device 1 changes the production sequence to perform staffing, and calculates the staffing and production sequence that maximize the evaluation value for evaluating productivity. The process plan making auxiliary device 1 outputs the process plan information indicating the staffing and production sequence that maximize the evaluation value for evaluating productivity.

[0031] Next, use Figure 2 The functional structure of the process plan making auxiliary device 1 is explained. The process plan making auxiliary device 1 has: an attendance information generating unit 11, which receives the shooting information from the camera 2, determines the operator who is on duty, that is, the attendance, based on the shooting information, and generates attendance information indicating the attendance; an attendance information storage unit 12, which stores the attendance information; an operation time information storage unit 13, which stores the operation time information indicating the operation time of each operator; an operation time information updating unit 14, which receives the shooting information from the camera 2, and updates the operation time information based on the shooting information; a production plan information acquiring unit 15, which accepts input of production plan information indicating the production plan for the day from the user; a production plan information storage unit 16, which stores the production plan information; and an operation definition information storage unit 17, which stores the operation definition information indicating the element operation, which constitutes each process of the production process of each model.

[0032] In addition, the process planning auxiliary device 1 has: a personnel allocation calculation unit 18, which calculates the personnel allocation for the operation corresponding to the production plan shown in the production plan information based on the attendance information, the operation time information and the operation definition information; a process planning information generation unit 19, which calculates the personnel allocation and production sequence that maximizes the evaluation value of evaluating productivity and generates process planning information; and a process planning information output unit 20, which outputs the process planning information.

[0033] The attendance information generation unit 11 identifies the person shown in the image shown in the shooting information received from the camera 2, thereby obtaining the name of the operator present, that is, the attendance name. The attendance information generation unit 11 generates attendance information indicating the attendance name and stores it in the attendance information storage unit 12. As a means of identifying the person shown in the image, there are, for example, a method of reading an AR (Argumented Reality) marker dedicated to the operator, a method of using facial recognition technology, and the like.

[0034] The work time information storage unit 13 stores work time information indicating the work time of each operator. Figure 3Describe the operation time information. Figure 3 In the example of , the operation time information is data that records the time taken for the element operation according to the combination of operator / machine type / element operation. Hereinafter, the time taken for the element operation is referred to as the element operation time. The element operation time shown in the operation time information is not a constant, but a continuous probability distribution with the element operation time as the horizontal axis and the number of times as the vertical axis. Figure 3 The probability distribution of the element operation time of the combinations of operator 1 / machine model M1 / element operation E01, operator 1 / machine model M1 / element operation E02, operator 2 / machine model M1 / element operation E01, and operator 2 / machine model M1 / element operation E02 is shown.

[0035] Regarding the combination of operator / machine type / element operation, if there is an actual operation performance in the past, the operation time information indicating the actual operation time of the element is stored. However, if there is no actual operation performance in the past (newcomer, supporter, etc.), the operation time information indicating the predicted operation time of the element is stored. The prediction method of the element operation time will be described later. In addition, regarding the combination of operator / machine type / element operation, when new operation performance is obtained, the operation information is updated.

[0036] return Figure 2 The staffing calculation unit 18 performs staffing to maximize the evaluation value of evaluating productivity based on the attendance information, operation time information, and operation definition information for the production plan (machine model and number of units) indicated by the production plan information. Staffing refers to the allocation of operators according to each element operation of each process that constitutes the production product. The evaluation value for evaluating productivity is, for example, the value obtained by dividing the number of units produced by the total time the operator is on the scene (units / hour). In this case, the larger the value (units / hour) obtained by dividing the number of units produced by the total time the operator is on the scene, the higher the productivity.

[0037] Here, use Figure 4 Describes the job definition information. Figure 4In the example, the operation definition information is information indicating what kind of element operations the production process of each model consists of. The production process of model M1 has four processes, namely "Preparation", "Process 1", "Process 2" and "Process 3". "Preparation" is composed of "element operation E01", ..., "element operation E0n0". "Process 1" is composed of "element operation E11", ..., "element operation E1n1". "Process 2" is composed of "element operation E21", ..., "element operation E2n2". "Process 3" is composed of "element operation E31", ..., "element operation E3n3". For example, suppose that process 1 is a subgroup, process 2 is a characteristic test, and process 3 is a general group. "Element operation E01" of "Preparation" is, for example, a fixture replacement, and element operation E0n0 is, for example, a recording operation. "Element operation E11" of "Process 1" is, for example, a fixture replacement, and element operation E1n1 is, for example, screw tightening. The "elementary operation E21" of "process 2" is, for example, replacement of a jig, and the elementary operation E2n2 is, for example, removal of the inspection machine. The "elementary operation E31" of "process 3" is, for example, replacement of a jig, and the elementary operation E3n3 is, for example, a conveying operation.

[0038] return Figure 2 , the process plan information generating unit 19 changes the production sequence of the machine model, and again causes the staffing calculation unit 18 to perform the staffing that maximizes the evaluation value. When the maximum evaluation value calculated by the staffing calculation unit 18 is higher than the previous calculation result, the optimal solution of the staffing and production sequence is updated. This process is repeated, and the process plan information generating unit 19 compares all the maximum evaluation values ​​calculated by the staffing calculation unit 18, sets the staffing and production sequence that maximizes the evaluation value as the optimal solution, and generates process plan information.

[0039] The process plan information output unit 20 outputs the process plan information generated by the process plan information generating unit 19. The process plan information may be output by, for example, a screen display or voice output, or may be sent to a user terminal used by a user (manager or operator).

[0040] The operation time information updating unit 14 determines the person and machine type shown in the image indicated by the photographing information received from the camera 2 and measures the element operation time. The operation time information updating unit 14 updates the operation time information stored in the operation time information storage unit 13 based on the measured element operation time.

[0041] use Figure 5The functional structure of the operation time information updating unit 14 is described. The operation time information updating unit 14 includes: a detection unit 141, which detects the operator and the model of the product being processed by the operator based on the image shown in the shooting information; an operation time measuring unit 142, which measures the element operation time of the operator; an updating unit 143, which updates the operation time information based on the element operation time measured by the operation time measuring unit 142; a person attribute information storage unit 144, which stores the person attribute information indicating the attributes of each operator who has had operation performance in the past; and an operation time prediction unit 145, which predicts the element operation time based on the person attribute information when the operator detected by the detection unit 141 has no operation performance in the past (newcomer, supporter, etc.).

[0042] The detection unit 141 detects the operator and the model of the product the operator is handling based on the image shown in the shooting information received from the camera 2. The detection unit 141 refers to the personal attribute information stored in the personal attribute information storage unit 144 to determine whether the detected operator is an operator with past work performance. In the case where the detected operator is an operator with past work performance, the detection unit 141 sends the information indicating the detected operator and model and the shooting information to the work time measurement unit 142. In the case where the detected operator is not an operator with past work performance (newcomer, supporter, etc.), the detection unit 141 sends the information indicating the detected operator and model and the shooting information to the work time prediction unit 145.

[0043] When the operation time measuring unit 142 receives the information indicating the detected operator and machine type and the shooting information from the detection unit 141, it determines the element operation being performed by the operator based on the image shown in the shooting information, and measures the element operation time of the operator. As a unit for determining the element operation, there is, for example, an action classification device. The action classification device can automatically divide the image of the continuous operation according to each element operation based on the image of the operator's operation, through AI-based judgment, and can obtain the element operation time. Alternatively, in the case where the element operation can be identified by the fixture, the AR marker dedicated to the fixture can also be read to determine the element operation. The operation time measuring unit 142 sends the information indicating the operator and machine type received from the detection unit 141 and the information indicating the calculated element operation time to the update unit 143.

[0044] The updating unit 143 updates the operation time information of the corresponding combination of operator / machine model / element operation in the operation information stored in the operation time information storage unit 13 based on the information indicating the operator and machine model and the information indicating the element operation time received from the operation time measuring unit 142 .

[0045] Here, use Figure 6The character attribute information is described below. The character attribute information stores the attributes of the operator and their values. Figure 6 In the example, the attributes of the operator are "name", "gender", "age group", "years of work", "physique", "job A evaluation", "job B evaluation" and "job C evaluation".

[0046] The numerical value of "gender" is 1 for male and 2 for female. The numerical value of "age group" is 1 for 10s, 2 for 20s, 3 for 30s, 4 for 40s, 5 for 50s, and 6 for 60 years and above. The numerical value of "number of years of work" is 1 for less than 10 years, 2 for 11 to 20 years, 3 for 21 to 30 years, 4 for 31 to 40 years, and 5 for 41 years and above. The numerical value of "physique" is 1 for thin, 2 for standard, and 3 for fat. "Work A evaluation" is an evaluation related to the speed of completing work A. Similarly, "Work B evaluation" and "Work C evaluation" are evaluations related to the speed of completing work B and work C, respectively. For example, work A, work B, and work C include obtaining parts, installation, screw tightening, electronic component insertion, bonding work, welding work, and conveying work. The numerical values ​​of "Work A evaluation", "Work B evaluation", and "Work C evaluation" are 3 for fast, 2 for standard, and 1 for slow. Evaluation values ​​of “task A evaluation”, “task B evaluation”, and “task C evaluation” are given according to a predetermined rule.

[0047] The value of the attribute of the operator of the character attribute information is not limited to Figure 6 For example, regarding "age group", it can also be classified more finely in units of 5 years, or it can be set to the age value itself. "Years of work" can also be set to the value of the number of years of work itself. "Physique" can also be set to the value of BMI calculated based on height and weight. The attributes can also include evaluations related to the speed of completing tasks other than Task A, Task B, and Task C. In addition, the character attribute information also includes the attributes and values ​​of workers who have no work performance in the past (newcomers, supporters, etc.).

[0048] return Figure 5 The operation time prediction unit 145 determines the operators with past operation results who are similar to the operators with no past operation results (newcomers, supporters, etc.) by a calculation formula based on the character attribute information. The operation time prediction unit 145 predicts the element operation time of the operators with no past operation results by referring to the operation information of the operators with past operation results who are similar to the operators with no past operation results (newcomers, supporters, etc.).

[0049] Specifically, use Figure 7 to Figure 9An example of predicting the element operation time of a newcomer X who has no past operation performance will be described. The operation time prediction unit 145 performs the following steps 1 to 4 to predict the element operation time of the newcomer X. In step 1, a person (an operator who has past operation performance) whose attributes are most similar to those of the newcomer X is determined. In step 2, the operation time information of the person is read from the operation time information storage unit 13. In step 3, the proficiency is calculated backwards. In step 4, the probability distribution of the element operation time in the corresponding machine model and corresponding element operation of the newcomer X is calculated.

[0050] First, step 1 is explained. Let the values ​​of the attributes of the new person X be a01, a02, ..., a0N. Figure 7 In the example, the attributes of newcomer X are "sex" 1 (male), "age group" 2 (20s), "years of work" 1 (less than 10 years), and "physique" 3 (fat). In addition, "job A evaluation" is 2, "job B evaluation" is 1, and "job C evaluation" is 3. Let the attribute values ​​of operator i (i = 1, 2, ..., N) be ai1, ai2, ..., aiN. At this time, the similarity Si of the attributes of newcomer X and operator i is expressed by the following mathematical formula 1.

[0051]

Mathematical formula 1

[0052]

[0053] The work time prediction unit 145 determines the worker i whose attribute similarity Si is the smallest as the worker whose attributes are most similar to those of the newcomer X. The coefficient kj (j=1, ..., n), (0≤kj≤1) is a weighting coefficient of each attribute included in the person attribute information, and the smaller the value of kj, the stronger the attribute affects the similarity.

[0054] When the new worker X actually performs the work and the work time measuring unit 142 measures the element work time, the work time prediction unit 145 updates the coefficient kj (j=1, ..., n) at a predetermined timing. The details of the updating method of the coefficient kj (j=1, ..., n) will be described later.

[0055] The work time prediction unit 145 gives, for example, (sex) k1=1.0, (age group) k2=0.5, (years of work) k3=0.5, (physique) k4=0.7, (work A evaluation) k5=0.1, (work B evaluation) k6=0.1, (work C evaluation) k7=0.1 as initial values ​​of coefficients kj (j=1, ..., n). In the initial values, the coefficients kj of attributes that are considered to have high correlation when predicting similarity can be reduced.

[0056] In addition, although the least square method is used in Mathematical Formula 1, the means for calculating the similarity is not limited to the least square method, and other methods may be used, for example, multiple regression analysis.

[0057] In step 2, the operation time prediction unit 145 reads the operation time information of the operator i whose attribute similarity Si is the smallest from the operation time information storage unit 13. The element operation time of the corresponding machine model and the corresponding element operation of the operator i selected in step 2 is a probability distribution formed after the operator i has mastered the element operation to a certain extent based on experience. The newcomer X does not have such experience. Therefore, in step 3, when predicting the element operation time of the newcomer X, the proficiency is calculated backwards. Generally speaking, the proficiency can be expressed by the following mathematical formula 2.

[0058]

Mathematical formula 2

[0059] y(x)=cp^a(x)

[0060] y(x) is the operating time of the element at the xth production, x ≥ 0. c is the operating time of the element at the first production, c ≥ 0. p is the proficiency, p = y(2x) / y(x). a(x) = logx / log2.

[0061] The operation time prediction unit 145 divides the probability distribution of the element operation time of the operator i having the highest attribute similarity with the newcomer X at the current time point (actual performance number x) by the proficiency p, thereby obtaining the probability distribution of the element operation time of the newcomer X. The proficiency p and the actual performance number x are values ​​maintained for each operator, and the proficiency information indicating the proficiency p and the actual performance number x of each operator is stored in the person attribute information storage unit 144. In addition, every time the operation performance increases, the value of the proficiency p indicated by the proficiency information is updated according to the performance.

[0062] In step 4, a graph of probability distribution of element operation time in corresponding machine models and element operations of newcomer X is calculated. The operation time prediction unit 145 repeats steps 1 to 4 in the same manner and calculates a graph of probability distribution of element operation time of newcomer X for all machine models and all element operations.

[0063] Figure 8 : is a diagram conceptually showing the process of predicting the element work time of newcomer X. Figure 8 In the example, the worker i having the smallest similarity Si with the newcomer X is worker 4. That is, the attributes of worker 4 among workers 1 to 4 are closest to the attributes of the newcomer X. The work time prediction unit 145 divides the probability distribution of the element work time of worker 4 having the highest similarity with the newcomer X at the current time by the proficiency rate p, thereby obtaining the probability distribution of the element work time of the newcomer X.

[0064] Fig. 9 1 is a diagram showing an example of a graph of probability distribution of element work time of a newcomer X predicted by the work time prediction unit 145. Fig. 9 In the example of , the probability distribution of the element operation time of the combination of newcomer X / machine model M1 / element operation E01 is shown.

[0065] The operation time prediction unit 145 stores the operation information indicating the predicted probability distribution of each element of the operation time of the newcomer X in the operation time information storage unit 13. When the operation performance of the newcomer X is accumulated a predetermined number of times, the operation time prediction unit 145 stores the operation information indicating the probability distribution of each element of the operation time of the performance of the newcomer X in the operation time information storage unit 13. The predetermined number of times is, for example, 50 times. When the operation information indicating the probability distribution of each element of the operation time of the performance is stored in the operation time information storage unit 13, the newcomer X becomes an operator with operation performance.

[0066] Here, the update method of the coefficient kj (j=1, ..., n) is described in detail. When generating the work time information of the newcomer X, the work time prediction unit 145 compares the element work time predicted in advance with the element work time based on the performance, evaluates the accuracy, and updates the coefficient kj (j=1, ..., n) used in the prediction of the similarity.

[0067] The probability density function predicted as the element operation time of the newcomer X is expressed as f0(t), and the probability density function obtained as the element operation time of the newcomer X's actual performance is expressed as fX(t). t(0≤t) is a real number representing the element operation time. First, the operation time prediction unit 145 evaluates the prediction accuracy. The operation time prediction unit 145 calculates the evaluation value V for evaluating the prediction accuracy based on the difference between the prediction and the actual performance using the following mathematical formula 3.

[0068]

Mathematical formula 3

[0069]

[0070] The work time prediction unit 145 stores the evaluation value V as a history. The higher the prediction accuracy, the smaller the evaluation value V becomes. When the predicted element work time and the actual element work time completely match, V=0.

[0071] Next, the work time prediction unit 145 sets the probability density function of the actual element work time of other workers i as fi(t), and uses the following mathematical formula 4 to calculate the similarity Vi of the element work time with other workers i (i=1, ..., N).

[0072]

Mathematical formula 4

[0073]

[0074] The worker with the smallest similarity Vi of the element work time (the person with the highest similarity of the element work time with the newcomer X) is set as worker Y. The work time prediction unit 145 calculates the attribute similarity Si with other workers i (i=1, ..., N) using the mathematical formula 1 that quantifies the attribute similarity. The work time prediction unit 145 calculates and updates the coefficients k1, ..., kn that satisfy the following mathematical formula 5.

[0075]

Mathematical formula 5

[0076]

[0077] In Mathematical formula 5, kj (j = 1, ..., n), (0 ≤ kj ≤ 1). As a method for calculating the coefficients k1, ..., kn that satisfy Mathematical formula 5, there is, for example, the following method: set each coefficient to 3 decimal places, repeatedly search within a limited number of executions, and find the optimal solution. By updating the coefficient kj (j = 1, ..., n) that minimizes the similarity SY of the attribute between the operator Y with the smallest similarity Vi of the element operation time, the similarity of the attribute between the newcomer X and the person with the highest similarity of the element operation time is also the highest, which can improve the prediction accuracy of the element operation time after the next time.

[0078] The process planning support device 1 also updates the person attribute information stored in the person attribute information storage unit 144 and the operation definition information stored in the operation definition information storage unit 17 at a predetermined timing.

[0079] The personal attribute information is updated whenever the values ​​of changing items such as "age group", "years of work", "physique", "job A evaluation", "job B evaluation", and "job C evaluation" are changed. In addition, when information indicating the date of birth and date of employment of the operator is stored as information attached to the personal attribute information, the process plan preparation supporting device 1 can automatically update the "age group" and "years of work". In addition, the process plan preparation supporting device 1 can cooperate with a health management system in a company that manages the health of the operator, thereby obtaining information indicating the physique of the operator and automatically updating the "physique".

[0080] Regarding the job definition information, the user edits and updates the job definition information when the structure of the element work of each step in the production process of each model is changed, for example, when the production of a new model is started or when the design of the product is changed.

[0081] In a structure where a user updates the job definition information, the user may forget to update it or may not notice the structural change of the element operation. Therefore, the process planning auxiliary device 1 may also be configured to notify the user when the structure of the element operation of each process in the production process of each model may be changed. For example, in the process planning auxiliary system 100, the camera 2 photographs the operator while the operator is performing the operation. The operation time information update unit 14 of the process planning auxiliary device 1 determines the operator, machine model, and element operation reflected in the image shown by the shooting information received from the camera 2. When the order of the element operations actually performed by the operator on the model determined by the operation time information update unit 14 is different from the order of the element operations of the corresponding model shown in the job definition information, the process planning auxiliary device 1 notifies the user that the structure of the element operation in the corresponding model may be changed.

[0082] Regarding the notification method for the user, for example, a message may be displayed on the screen, outputted by voice, or sent to a user terminal used by the user. In addition, a job definition information editing screen may be displayed together with the message to accept the editing performed by the user. In this case, in the job definition information editing screen, candidates for the element operations to be changed may also be displayed according to the order of the element operations actually performed by the operator on the determined model.

[0083] Next, use Fig.10 The flow of the process plan preparation support processing performed by the process plan preparation support device 1 is described. For example, when the process plan preparation support device 1 is powered on, Fig.10 The process plan preparation auxiliary processing shown starts. The production plan information acquisition unit 15 of the process plan preparation auxiliary device 1 determines whether the production plan information representing the production plan for the day has been input (step S11). When the production plan information has not been input (step S11: No), the processing is transferred to step S19. When the production plan information has been input (step S11: Yes), the production plan information acquisition unit 15 stores the input production plan information in the production plan information storage unit 16. The staffing calculation unit 18 performs staffing that maximizes the evaluation value of evaluating productivity for the production plan (machine model and number of units) shown in the production plan information based on the attendance information, the operation time information, and the operation definition information (step S12).

[0084] exist Figure 3 In the example of , the operation time information is data that records the element operation time for each combination of operator / machine type / element operation. The element operation time shown in the operation time information is not a constant, but a continuous probability distribution with the element operation time as the horizontal axis and the number of times as the vertical axis. Figure 3The probability distribution of the element operation time of the combinations of operator 1 / machine model M1 / element operation E01, operator 1 / machine model M1 / element operation E02, operator 2 / machine model M1 / element operation E01, and operator 2 / machine model M1 / element operation E02 is shown.

[0085] exist Figure 4 In the example, the operation definition information is information indicating what kind of element operations the production process of each model consists of. The production process of model 1 has four processes, namely "Preparation", "Process 1", "Process 2" and "Process 3". "Preparation" is composed of "element operation E01", ..., "element operation E0n0". "Process 1" is composed of "element operation E11", ..., "element operation E1n1". "Process 2" is composed of "element operation E21", ..., "element operation E2n2". "Process 3" is composed of "element operation E31", ..., "element operation E3n3".

[0086] return Fig.10 , the process plan information generating unit 19 determines whether the maximum evaluation value calculated by the staffing calculation unit 18 is higher than the previous calculation result (step S13). If it is not higher than the previous calculation result or there is no previous calculation result (step S13: No), the process transfers to step S15. If it is higher than the previous calculation result (step S13: Yes), the process plan information generating unit 19 updates the optimal solution of staffing and production sequence (step S14), and determines whether the staffing calculation unit 18 has calculated all the maximum evaluation values ​​(step S15).

[0087] If all the maximum evaluation values ​​have not been calculated (step S15: No), the process plan information generating unit 19 changes the production sequence of the machine model (step S16). The process returns to step S12, and the staffing calculation unit 18 is again made to perform the staffing that maximizes the evaluation value. Steps S12 to S16 are repeated. If all the maximum evaluation values ​​have been calculated by the staffing calculation unit 18 (step S15: Yes), the process plan information generating unit 19 compares all the maximum evaluation values ​​calculated by the staffing calculation unit 18, sets the staffing and production sequence that maximize the evaluation value as the optimal solution, and generates process plan information (step S17).

[0088] The process plan information output unit 20 outputs the process plan information generated by the process plan information generation unit 19 (step S18). If the power of the process plan preparation supporting device 1 is not turned off (step S19: No), the process returns to step S11, and steps S11 to S19 are repeated. If the power of the process plan preparation supporting device 1 is turned off (step S19: Yes), the process ends.

[0089] Next, use Fig.11 The flow of the operation time prediction process executed by the process plan preparation supporting device 1 is described. For example, when the detection unit 141 of the process plan preparation supporting device 1 detects that the operator has no past work performance, Fig.11 The work time prediction process shown starts. The work time prediction unit 145 of the process planning support device 1 specifies a person (a worker who has work performance in the past) whose attributes are most similar to those of the newcomer X (step S21).

[0090] exist Figure 8 In the example, the character with the most similar attributes to newcomer X is operator 4.

[0091] return Fig.11 , the work time prediction unit 145 reads the work time information of the determined person from the work time information storage unit 13 (step S22). The work time prediction unit 145 calculates the proficiency of the determined person (step S23). The probability distribution of the element work time of the determined person at the current time point (actual performance number x) is divided by the proficiency rate, thereby calculating the probability distribution of the element work time of the new person X (step S24), and the processing ends.

[0092] exist Fig. 9 In the example of , the probability distribution of the element operation time of the combination of newcomer X / machine model M1 / element operation E01 is shown.

[0093] According to the process plan making auxiliary device 1 of the first embodiment, when making a process plan that maximizes the evaluation value of productivity based on the element working time of each operator, the element working time of the operator with no working results in the past is predicted based on the working time information of the operator with working results in the past whose attributes are most similar to the operator with no working results in the past, and the working time information indicating the element working time of the operator with no working results in the past is generated, thereby making it possible to perform efficient work instructions even when there are operators who do not know the working time. In addition, the element working time is processed using probability distribution, so it is possible to cope with the situation where there is a deviation in the element working time.

[0094] (Implementation Method 2)

[0095] In the second embodiment, the work time prediction unit 145 uses a prediction process different from that in the first embodiment. The other configurations are the same as those in the first embodiment. The element work time of a certain worker is represented by an exponential function model having a negative exponent as shown in the following mathematical formula 6.

[0096]

Mathematical formula 6

[0097] y(x)=p -x +c

[0098] y(x) is the element operation time of the xth production, x ≥ 0. c is the element operation time of the 1st production, c ≥ 0. p is the proficiency. The element operation time of the xth production of a certain operator is set as T(x), and p that minimizes the following mathematical formula 7 is set as the proficiency of the operator.

[0099]

Mathematical formula 7

[0100]

[0101] Generally speaking, as the number of digits below the decimal point of the proficiency rate p increases, it takes more time to calculate, so the number of digits can be determined according to the accuracy required by the user. Similar to the first embodiment, the operation time prediction unit 145 divides the probability distribution of the element operation time of the operator with past operation performance at the current time point (number of performance x) who has the highest similarity in attributes with the operator with no past operation performance by the proficiency rate p, thereby obtaining the probability distribution of the element operation time of the operator with no past operation performance.

[0102] According to the process planning support device 1 of the second embodiment, the proficiency that minimizes the difference between the element working time calculated using the proficiency and the element working time of the actual performance is used to predict the element working time of the operator who has no previous work performance, thereby improving the prediction accuracy.

[0103] In the above-mentioned embodiments 1 and 2, the attendance information generating unit 11 determines the person shown in the image shown in the shooting information, thereby obtaining the name of the operator present, that is, the attendance name, and generates attendance information indicating the attendance name, but it is not limited to this. For example, the attendance information generating unit 11 may also determine the person shown in the static image of the operator, thereby obtaining the name of the operator present, that is, the attendance name, and generate attendance information indicating the attendance name. Alternatively, the process planning auxiliary device 1 may also cooperate with an entry and exit management system within a company that manages the entry and exit of operators, thereby obtaining attendance information.

[0104] In the above-mentioned embodiments 1 and 2, the process plan preparation auxiliary device 1 has the operation time information storage unit 13, the attendance information storage unit 12, and the operation definition information storage unit 17, but these storage units may also be owned by an external device or system. In addition, the operation time information storage unit 13 and the operation time information update unit 14 may also be owned by an operation time information update device different from the process plan preparation auxiliary device 1. Similarly, the attendance information storage unit 12 and the attendance information generation unit 11 may also be owned by an attendance information generation device different from the process plan preparation auxiliary device 1.

[0105] In the above-mentioned embodiments 1 and 2, the production plan information acquisition unit 15 of the process plan making auxiliary device 1 accepts the input of the production plan information indicating the production plan of the product of today, but is not limited to this. The user can input the production plan information indicating the production plan of the product on the date on which the process plan is desired to be made into the process plan making auxiliary device 1. Alternatively, the process plan making auxiliary device 1 can also be configured to cooperate with the production plan system within the company that manages the production plan, and when the user inputs the date (specified date) on which the process plan is desired to be made into the process plan making auxiliary device 1, the production plan information acquisition unit 15 obtains the production plan information indicating the production plan on the specified day from the production plan system. In addition, the unit for making a process plan is not limited to 1 day, and can also be other units such as half a day, 2 days, etc.

[0106] In the above-mentioned embodiments 1 and 2, an example of assisting in the preparation of a process plan that assigns tasks included in the process of producing a product to workers is described. However, the present invention is not limited to products, and any product may be a product that includes predetermined tasks in the production process.

[0107] use Fig.12 The hardware structure of the process planning support device 1 is described. Fig.12 As shown, the process plan preparation support device 1 includes a temporary storage unit 101, a storage unit 102, a calculation unit 103, an input unit 104, a transceiver unit 105, and a display unit 106. The temporary storage unit 101, the storage unit 102, the input unit 104, the transceiver unit 105, and the display unit 106 are connected to the calculation unit 103 via a bus.

[0108] The calculation unit 103 is, for example, a CPU (Central Processing Unit) and executes the processing of the attendance information generating unit 11 , the operation time information updating unit 14 , the staffing calculation unit 18 , and the process plan information generating unit 19 according to the control program stored in the storage unit 102 .

[0109] The temporary storage unit 101 is, for example, a RAM (Random-Access Memory) and loads the control program stored in the storage unit 102 , and is used as a work area of ​​the calculation unit 103 .

[0110] The storage unit 102 is a non-volatile memory such as a flash memory, a hard disk, a DVD-RAM (Digital Versatile Disc-Random Access Memory), or a DVD-RW (Digital Versatile Disc-ReWritable). The storage unit 102 stores in advance a program for causing the calculation unit 103 to perform the process planning support device 1, and supplies the data stored in the program to the calculation unit 103 according to the instruction of the calculation unit 103, and stores the data supplied from the calculation unit 103. The storage unit 102 is composed of an attendance information storage unit 12, an operation time information storage unit 13, a production plan information storage unit 16, and an operation definition information storage unit 17.

[0111] The input unit 104 is an input device such as a keyboard, a pointing device, and a voice input device, and an interface device that connects the input device to the bus. Information input by the user is supplied to the calculation unit 103 via the input unit 104. The input unit 104 functions as the production plan information acquisition unit 15. In a configuration in which the operation time information update unit 14 displays an edit screen of the operation definition information together with a message and accepts editing by the user, the input unit 104 functions as the operation time information update unit 14.

[0112] The transceiver 105 is a network terminal device or a wireless communication device connected to the network, and a serial interface or a LAN (Local Area Network) interface connected thereto. In a configuration in which the process plan information output unit 20 sends process plan information to a user terminal used by a user (manager or operator), the transceiver 105 functions as the process plan information output unit 20.

[0113] The display unit 106 is a display device such as an LCD (Liquid Crystal Display) or an organic EL (electroluminescence) display. In a configuration in which the process plan information output unit 20 displays the process plan information in a screen format, the transceiver unit 105 functions as the process plan information output unit 20. In a configuration in which the operation time information update unit 14 displays an edit screen of the operation definition information together with a message and accepts editing by the user, the display unit 106 functions as the operation time information update unit 14.

[0114] The control program uses the temporary storage unit 101, the calculation unit 103, the storage unit 102, the input unit 104, the transceiver unit 105, and the display unit 106 as resources to perform processing, thereby executing Figure 2The process planning auxiliary device 1 shown includes the attendance information generating unit 11, attendance information storage unit 12, operation time information storage unit 13, operation time information updating unit 14, production plan information acquisition unit 15, production plan information storage unit 16, operation definition information storage unit 17, staffing calculation unit 18, process plan information generating unit 19 and process plan information output unit 20.

[0115] In addition, the above-described hardware configuration and flowchart are merely examples and can be arbitrarily changed and modified.

[0116] The core parts for performing the processing of the process planning support device 1, such as the calculation unit 103, the temporary storage unit 101, the storage unit 102, the input unit 104, the transceiver unit 105, and the display unit 106, can be realized by using a general computer system regardless of a dedicated system. For example, a computer program for executing the above-mentioned actions may be stored in a computer-readable recording medium such as a floppy disk, a CD-ROM (Compact Disc-Read Only Memory), or a DVD-ROM (Digital Versatile Disc-Read Only Memory) and distributed, and the computer program may be installed in a computer, thereby constituting the process planning support device 1 that performs the above-mentioned processing. In addition, the computer program may be stored in a storage device of a server device on a communication network represented by the Internet, and downloaded by a general computer system, thereby constituting the process planning support device 1.

[0117] Furthermore, when the functions of the process planning support device 1 are realized by sharing the OS (Operating System) and the application program, or by cooperation between the OS and the application program, only the application program portion may be stored in a recording medium or a storage device.

[0118] In addition, the computer program can be superimposed on a carrier wave and provided via a communication network. For example, the computer program can be disclosed on a bulletin board (BBS: Bulletin Board System) on a communication network and provided via the communication network. Furthermore, the computer program can be started and executed like other application programs under the control of the OS, thereby enabling the processing to be performed.

[0119] In addition, the present invention can realize various embodiments and variations without departing from the broad spirit and scope of the present invention. In addition, the above-mentioned embodiments are used to illustrate the present invention and do not limit the scope of the present invention. That is, the scope of the present invention is not shown by the embodiments but by the claims. Moreover, various variations implemented within the claims and within the scope of the inventive meaning equivalent thereto are considered to be within the scope of the present invention.

[0120] This application is based on Japanese Patent Application No. 2022-079193 filed on May 13, 2022. The entire specification, claims, and drawings of Japanese Patent Application No. 2022-079193 are incorporated herein by reference.

[0121] Description of symbols

[0122] 1: process plan making auxiliary device; 2: camera; 11: attendance person information generating unit; 12: attendance person information storing unit; 13: operation time information storing unit; 14: operation time information updating unit; 15: production plan information acquiring unit; 16: production plan information storing unit; 17: operation definition information storing unit; 18: staffing calculation unit; 19: process plan information generating unit; 20: process plan information output unit; 100: process plan making auxiliary system; 101: temporary storage unit; 102: storage unit; 103: calculation unit; 104: input unit; 105: transceiver unit; 106: display unit; 141: detection unit; 142: operation time measuring unit; 143: updating unit; 144: person attribute information storing unit; 145: operation time prediction unit.

Claims

1. A process planning auxiliary device, the process planning auxiliary device comprising: a production plan information acquisition unit that acquires production plan information indicating a production plan for a product; a staffing calculation unit for calculating staffing for elemental work corresponding to the production plan indicated by the production plan information, based on attendance information indicating operators who are on duty, job definition information indicating elemental work of each process constituting a production process of a product, and job time information indicating the time taken for elemental work of each operator, namely, elemental work time; a process plan information generating unit that replaces the production sequence, causes the staffing calculation unit to calculate the staffing, and generates process plan information indicating the staffing and production sequence that maximizes the evaluation value for evaluating productivity; and A work time information updating unit updates the work time information of an operator who had work performance in the past based on shooting information representing an image of the operator, predicts the element work time of the operator who has no work performance in the past based on the work time information of the operator who has work performance in the past whose attributes are most similar to those of the operator who has no work performance in the past, and generates the work time information of the operator who has no work performance in the past.

2. The process planning support device according to claim 1, wherein: The operation time information updating unit comprises: A detection unit that detects the type of the worker and the product being processed by the worker based on the image shown in the photographed information; a work time measuring unit for measuring the element work time of the operator based on the photographing information; an updating unit that updates the operation time information of the operator who had the operation performance in the past, based on the operator detected by the detection unit and the type of the product being processed by the operator, and the element operation time measured by the operation time measuring unit, when the operator detected by the detection unit had the operation performance in the past; as well as The operation time prediction unit determines, when the operator detected by the detection unit has no operation performance in the past, an operator with operation performance in the past whose attributes are most similar to those of the operator with no operation performance in the past based on the personal attribute information representing the attributes of each operator, predicts the element operation time of the operator with no operation performance in the past based on the operation time information of the determined operator, and generates the operation time information representing the element operation time of the operator with no operation performance in the past.

3. The process planning support device according to claim 2, wherein: The element work time indicated by the work time information is a probability distribution of each element work time of each worker.

4. The process planning support device according to claim 3, wherein: The operation time prediction unit divides the probability distribution of each element of the operation time of the operator with past operation performance and whose attributes are most similar to the operator with no past operation performance at the current time by the proficiency rate of the operator with past operation performance and whose attributes are most similar to the operator with no past operation performance, thereby obtaining the probability distribution of each element of the operation time of the operator with no past operation performance.

5. The process plan preparation support device according to any one of claims 2 to 4, wherein: When the work performance of the operator who has no work performance in the past has accumulated a predetermined number of times, the work time prediction unit treats the operator who has no work performance in the past as the operator who has work performance in the past, and generates the work information based on the operator detected by the detection unit and the type of product being processed by the operator, and the element work time measured by the work time measurement unit.

6. The process plan preparation support device according to any one of claims 1 to 5, wherein: The process plan preparation support device further includes an attendee information generating unit that specifies the attendee based on the photographing information and generates the attendee information.

7. A process plan making support system, comprising a camera for photographing a worker, and a process plan making support device connected to the camera, The process plan making auxiliary device comprises: a production plan information acquisition unit that acquires production plan information indicating a production plan for a product; a staffing calculation unit for calculating staffing for elemental work corresponding to the production plan indicated by the production plan information, based on attendance information indicating operators who are on duty, job definition information indicating elemental work of each process constituting a production process of a product, and job time information indicating the time taken for elemental work of each operator, namely, elemental work time; a process plan information generating unit that replaces the production sequence, causes the staffing calculation unit to calculate the staffing, and generates process plan information indicating the staffing and production sequence that maximizes the evaluation value for evaluating productivity; and A work time information updating unit updates the work time information of an operator who had work performance in the past based on shooting information indicating an image of the operator shot by the camera, predicts the element work time of the operator who has no work performance in the past based on the work time information of the operator who has work performance in the past whose attributes are most similar to those of the operator who has no work performance in the past, and generates the work time information of the operator who has no work performance in the past.

8. A process plan making assisting method, the process plan making assisting method comprising the following steps performed by a process plan making assisting device: Obtaining production plan information indicating a production plan for a product; Calculate the staffing for the element work corresponding to the production plan indicated by the production plan information based on the attendance information indicating the workers who are on duty, the job definition information indicating the element work of each process constituting the production process of the product, and the job time information indicating the time taken for the element work of each worker, namely the element work time; Replacing the production sequence, executing the step of calculating the staffing, and generating process plan information representing the staffing and production sequence that maximizes the evaluation value of evaluating productivity; as well as Based on the shooting information representing the image of the operator, the working time information of the operator who had working performance in the past is updated, and based on the working time information of the operator who had working performance in the past whose attributes are most similar to the operator who had no working performance in the past, the element working time of the operator who had no working performance in the past is predicted, and the working time information of the operator who had no working performance in the past is generated.

9. A program that causes a computer to perform the following steps: Calculate the staffing for the element work corresponding to the production plan indicated by the acquired production plan information based on the attendance information indicating the workers who are on duty, the job definition information indicating the element work of each process constituting the production process of the product, and the job time information indicating the time taken for the element work of each worker, namely the element work time; Replacing the production sequence, executing the step of calculating the staffing, and generating process plan information representing the staffing and production sequence that maximizes the evaluation value of evaluating productivity; as well as Based on the shooting information representing the image of the operator, the working time information of the operator who had working performance in the past is updated, and based on the working time information of the operator who had working performance in the past whose attributes are most similar to the operator who had no working performance in the past, the element working time of the operator who had no working performance in the past is predicted, and the working time information of the operator who had no working performance in the past is generated.

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