Computer system, information processing method, and program
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- HITACHI LTD
- Filing Date
- 2023-12-01
- Publication Date
- 2026-07-30
AI Technical Summary
The installation of sensors in factories is costly and requires significant space, posing a challenge in balancing productivity improvement with installation costs within Cyber Physical Production Systems (CPPS).
A computer system that calculates the operation time of production processes, identifies processes needing sensor installation based on work variation indices, selects appropriate sensors, and generates a display for presenting the sensor installation plan, thereby optimizing sensor placement and reducing costs.
The system effectively presents a sensor installation plan that reduces costs and improves productivity by focusing sensor installation on processes with significant work variations, enhancing the accuracy of production management within CPPS.
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a technology for assisting the installation of sensors in a factory.
Background Art
[0002] In order to manage a factory that produces products, the use of a Cyber Physical Production System (CPPS) that analyzes the gap between the physical information of the factory and the cyber information in the virtual space and utilizes it for factory management is advancing.
[0003] By using the signals acquired from sensors, it is possible to monitor the progress of work, the occurrence of abnormalities, etc. in the factory. For example, Patent Document 1 describes that "a work monitoring device includes a signal acquisition unit 51 that acquires a detection signal from a sensor that monitors the movement of an object occurring at a work location in a process or the power supply of electric equipment arranged at the work location to detect the presence or absence of activity in the process, and a noise identification unit 14 that identifies a signal determined to be inappropriate as noise based on filter information indicating a determination condition for determining whether an activity state forming signal included in the acquired detection signal and forming an activity state or an inactivity state is appropriate as a start signal and / or an end signal of the process, a noise removal unit 15 that removes the identified noise, and a processing signal output means that outputs the detection signal from which the noise has been removed as a work signal. Therefore, a work time measurement system capable of automatically measuring the work time on an assembly line is realized."
Prior Art Documents
Patent Documents
[0004]
Patent Document 1
Summary of the Invention
Problems to be Solved by the Invention
[0005] By reflecting various physical information in the virtual space, the accuracy of production management using CPPS is improved. However, the installation of sensors is costly, and it is necessary to secure installation space.
[0006] Therefore, considering the trade-off relationship between productivity improvement and the cost required for sensor installation, it is necessary to install sensors.
[0007] The present invention provides a system and method for presenting a sensor installation plan that can suppress the cost required for sensor installation and efficiently improve productivity.
Means for Solving the Problems
[0008] A typical example of the invention disclosed in the present application is as follows. That is, a computer system including a processor and a storage device connected to the processor, the storage device storing process information for managing a plurality of production processes of a product produced in a factory, standard operation time information for managing the standard operation time of each production process, recommended sensor information for managing sensors used for monitoring each production process, and performance information storing a first performance including the time required for production of the product and a second performance including the operation time of at least one of the production processes. The processor calculates the operation time of the production process corresponding to the second performance using the second performance stored in the performance information, obtains the standard operation time of the production process in which the second performance is not recorded from the standard operation time information, calculates a variation index indicating the variation of work for each of the plurality of production processes using the first performance, the calculated operation time, and the standard operation time, identifies the production process in which the installation of the sensor is necessary based on the variation index, selects the sensor used for monitoring the identified production process with reference to the recommended sensor information, and generates display information for displaying the identified production process and the selected sensor.
Effects of the Invention
[0009] According to the present invention, it is possible to present an installation plan for sensors that can suppress the cost required for installing sensors and improve productivity efficiently. Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.
Brief Description of the Drawings
[0010]
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Modes for Carrying Out the Invention
[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the present invention is not construed as being limited to the description of the embodiments shown below. It will be easily understood by those skilled in the art that the specific configuration can be changed without departing from the spirit or gist of the present invention.
[0012] In the configuration of the invention described below, the same or similar configurations or functions are denoted by the same reference numerals, and duplicate explanations are omitted.
[0013] In this specification and the like, notations such as "first", "second", "third", etc. are attached to identify components, and do not necessarily limit numbers or order.
[0014] In the drawings and the like, the positions, sizes, shapes, and ranges of each component shown may not represent the actual positions, sizes, shapes, and ranges, etc. in order to facilitate the understanding of the invention. Therefore, in the present invention, it is not limited to the positions, sizes, shapes, and ranges, etc. disclosed in the drawings and the like.
Example
[0015] FIG. 1 is a diagram showing a configuration example of the production result management device of Example 1.
[0016] The production result management device 100 includes a processor 110, a storage device 111, an input device 112, an output device 113, and a communication device 114. Each hardware element is connected to each other via a bus 115.
[0017] The production result management device 100 manages the results (histories) of the production processes of products in a factory, and generates an installation plan for sensors for monitoring the production processes using the results. In addition, the production result management device 100 evaluates the cost and productivity when sensors are installed according to the sensor installation plan. In the following description, the production process of a product is described as a process.
[0018] Here, a product is produced through a plurality of processes. Also, the flow of the processes for producing a product is managed as a production line.
[0019] The communication device 114 is a device for connecting to an external device or an external system. In Example 1, the communication device 114 is connected to the terminal 101 via a network 102 such as a LAN (Local Area Network). The terminal 101 is a terminal operated by a user who uses the production result management device 100. Note that the production result management device 100 may be connected to a factory management system.
[0020] The input device 112 is a keyboard, a mouse, a touch panel, or the like. The output device 113 is a display or the like.
[0021] The processor 110 is an arithmetic unit that controls the entire production performance management device 100 and executes a program stored in the storage device 111. By executing processing according to the program, the processor 110 operates as a functional unit (module) that realizes a specific function. In the following description, when explaining the processing with the functional unit as the subject, it indicates that the processor 110 is executing a program that realizes the functional unit.
[0022] The processor 110 functions as a work variation estimation unit 120, a sensor configuration generation unit 121, a production simulation unit 122, a cost evaluation unit 123, a productivity evaluation unit 124, and a display unit 125.
[0023] The work variation estimation unit 120 estimates the magnitude of work variation in each process. The sensor configuration generation unit 121 identifies the processes that need to be monitored and generates a sensor setting proposal for monitoring the processes.
[0024] The production simulation unit 122 performs a production simulation of the product when sensors are installed. The cost evaluation unit 123 calculates the cost required to install sensors based on the sensor setting proposal. The productivity evaluation unit 124 evaluates the degree of improvement in productivity when sensors are installed based on the sensor installation proposal. An index representing the degree of improvement in productivity may be, for example, the number of products produced per unit time. The display unit 125 generates display information.
[0025] Note that the functional units of the production performance management device 100 may combine a plurality of functional units into one functional unit, or divide one functional unit into a plurality of functional units for each function.
[0026] The memory device 111 is a memory or the like, and stores programs executed by the processor 110 and various types of information. Note that the production result management device 100 may include a large-capacity storage device such as an HDD (Hard Disk Drive) and an SSD (Solid State Drive).
[0027] The memory device 111 stores process information 130, production resource information 131, performance information 132, standard operation time information 133, operation variation information 134, recommended sensor information 135, and sensor information 136.
[0028] The process information 130 is information for managing processes. The production resource information 131 is information for managing resources for performing operations in the process. Resources are, for example, people and robots. The performance information 132 is information for managing the performance of the production process of products. The standard operation time information 133 is information for managing the standard operation time of the process. The operation variation information 134 is information for managing the variation of operations in the process. The recommended sensor information 135 is information for managing sensors that can be used for monitoring the process. The sensor information 136 is information for managing sensors installed on the production line of products.
[0029] Next, with reference to FIGS. 2 to 8, the information managed by the production result management device 100 will be described.
[0030] FIG. 2 is a diagram showing an example of the process information 130 of the first embodiment.
[0031] The process information 130 stores a table 200 for each production line. The table 200 stores entries including a product ID 201, a component ID 202, a process ID 203, and a production resource ID 204. There is one entry for each combination of product and process.
[0032] Product ID 201 is a field that stores the ID of a product. Part ID 202 is a field that stores the IDs of the parts that make up the product. In Part ID 202, the IDs of the parts used in the operations of the process are stored. Note that in the case of a process that does not use parts, Part ID 202 is blank. Process ID 203 is a field that stores the ID of a process. And Production Resource ID 204 is a field that stores the ID of the resource that performs the operations of the process.
[0033] Figure 3 is a diagram showing an example of the production resource information 131 of Example 1.
[0034] The production resource information 131 stores a table 300 for each production line. Table 300 stores entries including a process ID 301, a production resource ID 302, and an attribute 303. There is one entry for each combination of process and resource.
[0035] Process ID 301 is a field that stores the ID of a process. Production Resource ID 302 is a field that stores the ID of a resource. In Production Resource ID 302, the IDs of the resources that can perform the operations of the process are stored. There may be multiple entries for one process. Attribute 303 is a field that stores the attributes of a resource.
[0036] Figure 4 is a diagram showing an example of the performance information 132 of Example 1.
[0037] The performance information 132 stores a table 400 for each production line. Table 400 stores entries including a product ID 401, a part ID 402, a process ID 403, a start time 404, an end time 405, and a sensor 406. There is one entry for each combination of product and process.
[0038] The product ID 401 is a field for storing the ID of the product. The component ID 402 is a field for storing the ID of the component. The process ID 403 is a field for storing the ID of the process. The start time 404 and the end time 405 are fields for storing the start time and the end time of the operation of the process. The sensor 406 is a field for storing the information of the sensor for monitoring the process. In this embodiment, it is assumed that the start time and the end time can be known based on the measurement result of the sensor.
[0039] Note that the performance information 132 also stores a table for managing the production time of the product.
[0040] Figure 5 is a diagram showing an example of the standard operation time information 133 of Example 1.
[0041] The standard operation time information 133 stores a table 500 for each production line. The table 500 stores entries including the product ID 501, the component ID 502, the process ID 503, and the standard operation time 504. There is one entry for each combination of product and process.
[0042] The product ID 501 is a field for storing the ID of the product. The component ID 502 is a field for storing the ID of the component. The process ID 503 is a field for storing the ID of the process. The standard operation time 504 is a field for storing the standard operation time of the operation of the process.
[0043] Figure 6 is a diagram showing an example of the operation variation information 134 of Example 1.
[0044] The operation variation information 134 stores a table 600 for each production line. The table 600 stores entries including the process ID 601 and the variation coefficient 602. There is one entry for each process.
[0045] The process ID 601 is a field for storing the ID of the process. The variation coefficient 602 is a field for storing the coefficient representing the variation of the operation of the process.
[0046] In this embodiment, it is assumed that the production time of the product is calculated by formula (1).
[0047]
Number
[0048] PT represents the production time. The subscript i represents the process ID. T i represents the standard operation time of the process. V i represents the variation coefficient of the process.
[0049] FIG. 7 is a diagram showing an example of the recommended sensor information 135 of Example 1.
[0050] The recommended sensor information 135 stores a table 700 for each production line. The table 700 stores entries including a process ID 701, a sensor 702, an improvement expectation value 703, and a cost 704. There is one entry for each process.
[0051] The process ID 701 is a field for storing the process ID. The sensor 702 is a field for storing sensors available for monitoring the operations of the process. There may be multiple entries for each process. The improvement expectation value 703 is a field for storing the improvement expectation value of the variation coefficient due to the installation of the sensor. The cost 704 is a field for storing the cost required for installing the sensor. The cost 704 in this embodiment stores a monetary cost.
[0052] FIG. 8 is a diagram showing an example of the sensor information 136 of Example 1.
[0053] The sensor information 136 stores a table 800 for each production line. The table 800 stores entries including a process ID 801 and a sensor 802. There is one entry for each process.
[0054] The process ID 801 is a field for storing the ID of a process. The sensor 802 is a field for storing sensors installed for monitoring the work of a process.
[0055] Next, the processing executed by the production result management device 100 will be described.
[0056] The production result management device 100 receives the input of results from the terminal 101 and stores them in the result information 132. When the production result management device 100 is connected to the factory management system, the production result management device 100 can obtain the results from the management system.
[0057] When the production result management device 100 receives a sensor configuration presentation instruction from the terminal 101, it executes a sensor configuration presentation process. FIG. 9 is a flowchart for explaining an example of the sensor configuration presentation process executed by the production result management device 100 of the first embodiment. In the sensor configuration presentation process, the following-described processes are executed for each production line. Note that the user may specify the production line or product to be processed.
[0058] The work variation estimation unit 120 calculates a variation coefficient (step S901). Specifically, the following processes are executed.
[0059] (S901-1) The work variation estimation unit 120 reads out the table 800 of the production line to be processed from the result information 132.
[0060] (S901-2) The work variation estimation unit 120 identifies the processes in which results exist, and calculates the work time based on the results of those processes. The work time can be calculated using the start time 404 and end time 405 of the entries that are the results. When there are multiple results for the same process, the work variation estimation unit 120 calculates the average value of the work times calculated from each result.
[0061] (S901-3) The operation variation estimation unit 120 reads the table 200 of the production line from the process information 130, and reads the table 500 of the production line to be processed from the standard operation time information 133.
[0062] (S901-4) The operation variation estimation unit 120 calculates the variation coefficient of each process by solving a mathematical optimization problem using Equation (2).
[0063]
Number
[0064] The subscript j represents the ID of the process without performance, and the subscript k represents the ID of the process with performance. AT k represents the calculated operation time. In the mathematical optimization problem, for example, the variation coefficient V is calculated so that the difference between the production time calculated from Equation (2) and the actual production time is minimized. j is calculated. Also, the variation coefficient of the process with performance is calculated using Equation (3).
[0065]
Number
[0066] (S901-5) The operation variation estimation unit 120 reflects the calculation result in the table 600 of the production line to be processed stored in the operation variation information 134.
[0067] For the process without performance, the operation time can be estimated using the standard operation time information 133. Thus, even when there is no performance for all processes, the operation variation can be estimated.
[0068] The above is the description of step S901.
[0069] Next, the sensor configuration generation unit 121 selects a process to be the installation target of the sensor based on the variation coefficient (step S902). For example, the sensor configuration generation unit 121 selects the process with the largest variation coefficient. Also, the sensor configuration generation unit 121 may select a process whose variation coefficient is greater than the threshold value. Further, the sensor configuration generation unit 121 may select a process whose variation coefficient is greater than the threshold value and where the sensor is not installed. A process where the sensor is not installed is a process for which there is no record in the table 400.
[0070] Next, the sensor configuration generation unit 121 determines a recommended sensor for monitoring the selected process (step S903).
[0071] Specifically, the sensor configuration generation unit 121 reads the table 700 of the production line to be processed from the recommended sensor information 135, and searches for an entry for the selected process. The sensor configuration generation unit 121 determines the sensor set in the sensor 702 of the retrieved entry as the recommended sensor.
[0072] If multiple entries are retrieved, the sensor configuration generation unit 121 selects an entry based on either the improvement expectation value 703 or the cost 704. For example, it is conceivable to select the entry with the largest improvement expectation value 703.
[0073] The sensor configuration generation unit 121 reads the table 800 of the production line to be processed from the sensor information 136, grasps the sensors that are already installed, and may exclude the sensors that are already installed from the candidates.
[0074] Next, the production simulation unit 122 calculates the variation coefficient of the work of the target process when the determined recommended sensor is installed (step S904).
[0075] Specifically, the production simulation unit 122 calculates the variation coefficient using the variation coefficient 602 of the selected process entry in the table 600 and the improvement expected value 703 of the determined sensor entry in the table 700. For example, a calculation method of subtracting the improvement expected value from the variation coefficient can be considered.
[0076] Next, the production simulation unit 122 executes a production simulation (step S905). Specifically, the following processing is executed.
[0077] (S905-1) The production simulation unit 122 reads the table 800 of the production line to be processed from the sensor information 136, and generates the simulation table 800 by reflecting the processing result of step S903 in the table 800.
[0078] (S905-2) The production simulation unit 122 sets a simulation model. The simulation model includes the variation coefficient as a parameter.
[0079] (S905-3) The production simulation unit 122 simulates the production of products on the production line using the model and the table 800.
[0080] The above is the description of the processing in step S905.
[0081] Next, the productivity evaluation unit 124 calculates an evaluation index based on the simulation table 800 and the result of the production simulation (step S906). Specifically, the following processing is executed.
[0082] (S906-1) The productivity evaluation unit 124 compares the original table 800 and the simulation table 800 to identify the added sensors.
[0083] (S906-2) The productivity evaluation unit 124 refers to the table 700 of the production line to be processed in the recommended sensor information 135 and searches for an entry corresponding to the added sensor. The productivity evaluation unit 124 acquires the cost 704 of the searched entry. The productivity evaluation unit 124 calculates the total value of the cost 704 as a cost index.
[0084] (S906-3) The productivity evaluation unit 124 calculates, based on the result of the production simulation, the number of products generated per unit time, or the number of products produced within a certain time, etc. as a productivity index.
[0085] Next, the display unit 125 generates and outputs display information based on the simulation table 800 and the evaluation index (step S907).
[0086] FIG. 10 is a diagram showing an example of a screen presented by the production performance management device 100 of the first embodiment to the terminal 101.
[0087] The screen 1000 includes an input field 1001 and an operation button 1002. The input field 1001 is a field for inputting a production line. The user inputs the identification information of the production line into the input field 1001 and presses the operation button 1002. When the display unit 125 receives the operation, it displays the table 1003, the display columns 1004, 1005 based on the display information. The table 1003 corresponds to the simulation table 800. The display column 1004 is a column for displaying the cost index. The display column 1005 is a column for displaying the productivity index.
[0088] Note that the production performance management device 100 may generate installation plans for a plurality of sensors, and perform production simulation and calculation of evaluation indexes for each sensor installation plan.
[0089] As described above, the production result management device 100 identifies a process with large work variations and generates a plan for installing sensors for monitoring the process. By installing sensors focused on the process with large work variations, productivity can be improved efficiently while suppressing costs.
[0090] Note that the present invention is not limited to the above-described embodiments and includes various modifications. Also, for example, the above-described embodiments have been described in detail for the sake of easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Also, for a part of the configuration of each embodiment, addition, deletion, or replacement with other configurations is possible.
[0091] In addition, each of the above configurations, functions, processing units, processing means, etc. may be realized in hardware by designing a part or all of them, for example, by using an integrated circuit. Also, the present invention can be realized by a program code of software that realizes the functions of the embodiments. In this case, a storage medium recording the program code is provided to a computer, and a processor included in the computer reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code itself and the storage medium storing it constitute the present invention. As a storage medium for supplying such a program code, for example, a flexible disk, a CD-ROM, a DVD-ROM, a hard disk, an SSD (Solid State Drive), an optical disk, a magneto-optical disk, a CD-R, a magnetic tape, a non-volatile memory card, a ROM, etc. are used.
[0092] Also, the program code for realizing the functions described in this embodiment can be implemented in a wide range of programs or script languages such as assembler, C / C++, perl, Shell, PHP, Python, Java (registered trademark), etc.
[0093] Furthermore, the program code of the software that realizes the functions of the embodiments may be distributed via a network, stored in a storage means such as a hard disk or memory of a computer or a storage medium such as a CD-RW or CD-R, and the processor included in the computer may read and execute the program code stored in the storage means or the storage medium.
[0094] In the above embodiments, the control lines and information lines show those considered necessary for explanation, and not necessarily all the control lines and information lines are shown on the product. All the components may be interconnected.
Explanation of Signs
[0095] 100 Production result management device 101 Terminal 102 Network 110 Processor 111 Storage device 112 Input device 113 Output device 114 Communication device 115 Bus 120 Work variation estimation unit 121 Sensor configuration generation unit 122 Production simulation unit 123 Cost evaluation unit 124 Productivity evaluation unit 125 Display unit 130 Process information 131 Production resource information 132 Performance information 133 Standard operation time information 134 Work variation information 135 Recommended sensor information 136 Sensor information 1000 Screen
Claims
1. A computer system, comprising a processor and a storage device connected to the processor, wherein the storage device stores process information for managing a plurality of production processes of products produced in a factory, standard operation time information for managing the standard operation time of each production process, recommended sensor information for managing sensors used for monitoring each production process, and performance information storing a first performance including the time required for producing the product and a second performance including the operation time of at least one of the production processes, wherein the processor uses the second performance stored in the performance information to calculate the operation time of the production process corresponding to the second performance, acquires the standard operation time of the production process in which the second performance is not recorded from the standard operation time information, uses the first performance, the calculated operation time, and the standard operation time to calculate a variation index indicating the variation of work for each of the plurality of production processes, identifies, based on the variation index, the production processes in which installation of the sensors is necessary, selects, with reference to the recommended sensor information, the sensors used for monitoring the identified production processes, and generates display information for displaying the identified production processes and the selected sensors. A computer system characterized by the above.
2. The computer system according to claim 1, wherein the processor identifies, based on a comparison result between the variation index and a threshold value, the production processes with large work variations. A computer system characterized by the above.
3. The computer system according to claim 2, wherein the processor calculates the variation index by solving a mathematical optimization problem using the first performance, the calculated operation time, and the standard operation time. A computer system characterized by the above.
4. The computer system according to claim 3, wherein the recommended sensor information stores data including the production process, the sensor, and an expected improvement value of the variation index of the production process due to installation of the sensor, wherein the processor corrects the variation index of the production process when the selected sensor is installed for the identified production process, performs a simulation of the production of the product using the variation index as a parameter, and calculates the cost required for installing the selected sensor for the identified production process. Based on the results of the simulation, calculate an evaluation index for evaluating the productivity of the product, A computer system, characterized by generating the identified production process, the selected sensor, the cost, and the display information for displaying the evaluation index.
5. The computer system according to claim 4, wherein the processor presents a screen based on the display information.
6. An information processing method executed by a computer system, wherein the computer system has a processor and a storage device connected to the processor, the storage device stores process information for managing a plurality of production processes of a product produced in a factory, standard operation time information for managing the standard operation time of each production process, recommended sensor information for managing sensors used for monitoring each production process, and performance information storing a first performance including the time required for producing the product and a second performance including the operation time of at least one of the production processes, the information processing method includes a first step in which the processor calculates the operation time of the production process corresponding to the second performance using the second performance stored in the performance information; a second step in which the processor obtains the standard operation time of the production process in which the second performance is not recorded from the standard operation time information; a third step in which the processor calculates a variation index indicating the variation of work for each of the plurality of production processes using the first performance, the calculated operation time, and the standard operation time; a fourth step in which the processor identifies the production process in which installation of the sensor is necessary based on the variation index; a fifth step in which the processor selects the sensor used for monitoring the identified production process with reference to the recommended sensor information; and a sixth step in which the processor generates display information for displaying the identified production process and the selected sensor.
7. The information processing method according to claim 6, wherein the fourth step includes a step in which the processor identifies the production process with large work variation based on the comparison result between the variation index and a threshold value.
8. The information processing method according to claim 7, The third step includes the processor calculating the variation index by solving a mathematical optimization problem using the first performance, the calculated working time, and the standard working time. An information processing method characterized by this.
9. An information processing method according to claim 8, The recommended sensor information stores data including an improvement expected value of the variation index of the production process due to the production process, the sensor, and the installation of the sensor, The information processing method is, a step of the processor correcting the variation index of the production process when the selected sensor is installed for the specified production process; a step of the processor executing a simulation of the production of the product using the variation index as a parameter; a step of the processor calculating the cost required to install the selected sensor for the specified production process; a step of the processor calculating an evaluation index for evaluating the productivity of the product based on the result of the simulation, and includes: The sixth step includes the processor generating display information for displaying the specified production process, the selected sensor, the cost, and the evaluation index. An information processing method characterized by this.
10. An information processing method according to claim 9, including a step of the processor presenting a screen based on the display information. An information processing method characterized by this.
11. A program for causing a computer having a processor and a storage device connected to the processor to execute, The storage device stores process information for managing a plurality of production processes of products produced in a factory, standard working time information for managing the standard working time of each production process, recommended sensor information for managing sensors used for monitoring each production process, and performance information storing a first performance including the time required for the production of the product and a second performance including the working time of at least one of the production processes, The program is, a first procedure for calculating the working time of the production process corresponding to the second performance using the second performance stored in the performance information; a second procedure for obtaining the standard working time of the production process in which the second performance is not recorded from the standard working time information; A third procedure of calculating a variation index indicating the variation of work for each of the plurality of production processes using the first achievement, the calculated working time, and the standard working time; A fourth procedure of identifying the production processes that require installation of the sensor based on the variation index; A fifth procedure of selecting the sensor to be used for monitoring the identified production processes with reference to the recommended sensor information; A sixth procedure of generating display information for displaying the identified production processes and the selected sensor, and a program characterized by causing the computer to execute the procedures.
12. The program according to claim 11, wherein the fourth procedure includes a procedure of identifying the production processes with large work variations based on the comparison result between the variation index and a threshold value.
13. The program according to claim 12, wherein the third procedure includes a procedure of calculating the variation index by solving a mathematical optimization problem using the first achievement, the calculated working time, and the standard working time.
14. The program according to claim 13, wherein the recommended sensor information stores data including the production process, the sensor, and the expected improvement value of the variation index of the production process by installation of the sensor, The program, a procedure of correcting the variation index of the production process when the selected sensor is installed for the identified production process; a procedure of executing a simulation of the production of the product using the variation index as a parameter; a procedure of calculating the cost required for installing the selected sensor for the identified production process; a procedure of calculating an evaluation index for evaluating the productivity of the product based on the result of the simulation, and causing the computer to execute the procedures, wherein the sixth procedure includes a procedure of generating the display information for displaying the identified production process, the selected sensor, the cost, and the evaluation index.
15. The program according to claim 14, including a procedure of causing the computer to present a screen based on the display information.