JP2020170316A - Information processing apparatus, production information operating method, and production information operating program - Google Patents

Abstract

To provide an information processing apparatus, an information processing method, and an information processing program capable of reducing the number of production performance data for calculating a degree of correlation with the KPI.SOLUTION: The information processing apparatus includes a calculation unit for calculating a representative value for each production performance parameter of stored production performance data, a selection unit for selecting only either one of the production performance data with a value that is smaller or larger than the representative value calculated by the calculation unit for each of the production performance parameters based on a master table, and an arithmetic operation unit for arithmetically operating a correlation degree between the production performance and the KPI for each production performance parameter using the production performance data selected by the selection unit.SELECTED DRAWING: Figure 8

Description

This case relates to an information processing device, a production information calculation method, and a production information calculation program.

On the production line, there is a difference between the production plan and the actual production. Therefore, a technique for improving a production line by using an index such as KPI is disclosed so that the production performance is improved (see, for example, Patent Documents 1 to 5).

JP-A-2018-195308 Japanese Unexamined Patent Publication No. 2017-162044 JP-A-2009-3794 Japanese Unexamined Patent Publication No. 2005-1587819 JP-A-2018-156346

In order to greatly improve the KPI, it is desirable to calculate the degree of correlation between each production record parameter and the KPI for the production record data. However, if the number of production record data for calculating the correlation degree is large, it takes time for the calculation process for calculating the correlation degree.

In one aspect, it is an object of the present invention to provide an information processing apparatus, an information processing method, and an information processing program capable of reducing the number of production record data for calculating the degree of correlation with KPI. ..

In one embodiment, the information processing apparatus calculates a representative value for each production record parameter for the accumulated production record data, and the calculation unit calculates each production record parameter based on the master table. Using the selection unit that selects only one of the production record data smaller than the representative value and the production record data that is larger than the representative value, and the production record data selected by the selection section, the KPI related to the production record for each production record parameter. It is provided with a calculation unit that calculates the degree of correlation with.

It is possible to reduce the number of production record data for calculating the degree of relevance to KPI.

(A) is a schematic diagram of a production line, and (b) is a diagram illustrating a case where products having different working times and step change times are flown to a plurality of production lines. It is a figure which illustrates the flow of the process of digital planning. It is a figure which illustrates the flow of the process of digital planning. It is a figure which illustrates the change rate of KPI with respect to the change of each production record parameter. (A) is a block diagram illustrating the overall configuration of the information processing apparatus according to the first embodiment, and (b) is a block diagram illustrating the hardware configuration of the information processing apparatus. It is a figure which illustrates the flowchart which shows an example of the processing executed by an information processing apparatus. (A) is a diagram illustrating a master table, and (b) and (c) are diagrams illustrating a range of production record data to be selected. It is a figure which exemplifies the production record data selected for each adjustment parameter.

Prior to the explanation of the examples, an outline of improvement activities using a key performance indicator (KPI: Key Performance Indicator) will be described.

As an example, in a factory production line, a plurality of operations are required to assemble a product, and a person or a robot performing the plurality of operations is assigned. FIG. 1A is a schematic view of a production line. In the production line, the product 1 to be produced is placed on the belt conveyor 2. Product 1 is an unfinished product and is a work target of each process worker. The product 1 is sequentially conveyed to each process by the belt conveyor 2. Humans and robot workers are assigned to each process.

It is preferable that each work is performed within a fixed working time. However, the work may be prolonged, or the work may have to be interrupted (pause) due to a malfunction of the production equipment, and the work may be performed beyond the work time. The time actually required for each worker to perform the work is called a cycle time (CT). If the CT is longer than the predetermined working time, the production completion time will be delayed. Among CTs, the cycle time when the worker is a robot is called MCT. The average value of CT and MCT is preferably short because good production is performed when the average value is short. Further, if the variation of CT and MCT is small, each working time is leveled, so that it is preferable that the variation is small.

For example, an indicator light 3 is arranged in each process. The indicator light 3 is an indicator light for notifying an alarm. When a problem occurs in the work, the work is temporarily interrupted (paused), and the indicator light 3 of the same process lights up. When the indicator light 3 is turned on, the support worker can grasp the process in which support is required. After that, the support worker supports the process. The average value of the recovery time for recovering the pause is preferably short because the effect on the production process is small if it is short. Further, if the variation in recovery time is small, each recovery time is leveled, so that it is preferable that the variation is small. If the frequency of pauses is small, it means that a good production process is being performed, so it is preferable that the frequency is small. From the viewpoint of accelerating the production completion time, it is preferable that the number of support workers (number of resources) is large. In addition, when a plurality of pauses overlap, a good production process can be performed by considering the priority of the support order by the support workers.

Next, when high-mix low-volume production is carried out on one production line, the types of products will change many times. Therefore, every time the type of product is changed, the setting of the production apparatus is changed. Such a setting change is referred to as "step change". The setup change is sometimes called "setup". If the step change time required for the step change is short, a good production process is performed, and therefore a short step time is preferable. If the variation in the step change time is small, each step change time is leveled, so that it is preferable.

FIG. 1B is a diagram illustrating a case where products having different working times and step-changing times are sent to a plurality of production lines. The work is stopped while the step change work is being performed. The step change time varies from product to product. When one person performs a plurality of stage changes, the production completion time can be advanced by devising the priority of the stage change work. From the viewpoint of accelerating the production completion time, it is preferable that the number of support workers (number of resources) is large. In addition, when a plurality of stage change operations overlap, the production completion time can be advanced by considering the priority of the stage change order by the support workers.

In the above-mentioned complicated production line, it is important to obtain a quantitative index for judging whether or not a good production process is performed. Table 1 is a diagram illustrating KPIs in a production line. There are various types of KPIs. For example, KPIs include operating KPIs and quality KPIs. For example, as illustrated in Table 1, the KPI of the operating system includes load efficiency, production end time, overtime hours, non-operating hours, work duplication rate, and the like. Load efficiency is the ratio of operating time (time during work) to total production time. The higher the load efficiency, the better. The production end time is the time when the production is completed. The earlier the production end time is preferable. Overtime hours are the hours obtained by subtracting the actual working hours from the actual working hours. It is preferable that the overtime hours are short. The non-operating time is the work waiting time. It is preferable that the non-operating time is short. The work duplication rate is the average time when the number of work duplications is totaled for each hour. The work duplication rate is preferably small.

In improvement activities based on KPIs, for example, production managers rely on know-how and intuition to determine improvement measures that improve KPIs and deploy them at production sites. However, if the production manager is absent or misunderstood, it will take time to repeat trial and error.

Therefore, it is conceivable to decide improvement measures by digital planning. Digital planning is to virtually verify the KPI change when the value of each production performance parameter is assigned.

FIG. 2 is a diagram illustrating a flow of processing in digital planning. As illustrated in FIG. 2, first, plan data and actual data are input. The planning data includes input plans, stage changes, and the like. The input plan is a plan for completing production, and is the type (type) of the product to be produced, the work content required for each product, the work time determined for each work, the number of products, and each production. The order of input to the line, the timing of input to each production line, etc. The step change of the plan data is the content of the work required for the step change. The actual data includes production actual data such as operation information, stage change information, and error information. The operation information is a statistic such as the average and variation of the work time actually required for each work. The step change information is information about the step change actually performed. The error information is information related to a defect of each work such as a pause.

Next, digital planning is performed. In digital planning, first, statistical processing is performed for each of the production performance data. Next, a virtual actual result is constructed by adjusting the statistical value obtained by the statistical processing as a production actual result parameter. In the example of FIG. 2, it is assumed that the average value of the working time when a specific person works on the product of the product type A001 is 5 minutes and the variation σ of the working time is 2 minutes for the equipment number # 1. Build virtual actual data when these values are changed. As a result, the priority of the restoration work of the suspension, the priority of the step change, and the like are determined, and each work and the step change are rearranged. Next, the KPI is calculated for the virtual actual data after sorting. The calculated KPI is output and displayed on a display device or the like. By comparing the KPI calculated from the production actual data with the KPI calculated from the virtual actual data, it can be determined whether or not the virtual actual data has been improved. If the virtual performance data is improved, it will be possible to determine what should be improved.

However, in digital planning, there is a problem that the number of production actual parameters increases, and it is difficult to specify which production actual parameters should be adjusted to improve the KPI. Therefore, we pay attention to the correlation between each production performance parameter and KPI. As illustrated in FIG. 3, each production performance parameter and KPI have a monotonous increase or monotonous decrease relationship. Therefore, it is conceivable to calculate the correlation coefficient between each production record parameter and the KPI, and obtain the sensitivity (correlation degree) of each production record parameter from the slope of the approximate line in that case. The greater the slope of the approximation line, the greater the degree of correlation. Once the correlation degree of each production performance parameter is acquired, the KPI can be greatly improved by adjusting the production performance parameter having a large correlation degree.

Therefore, as illustrated in FIG. 4, the rate of change of KPI with respect to the change of each production actual parameter is calculated as the degree of correlation. Sort the production performance parameters in descending order of correlation. In the example of FIG. 4, the degree of correlation of the step average is the highest. Therefore, by adjusting this step-by-step average, KPI can be improved efficiently.

In order to calculate the degree of correlation with KPI, a large amount of production record data is used for calculation. If the number of production record data required to calculate the degree of correlation is large, the calculation process will take time.

In the following embodiment, information processing that can shorten the time required to calculate the degree of correlation between each production record parameter and KPI by reducing the number of production record data for calculating the degree of correlation with KPI. The device, the production information calculation method, and the production information calculation program will be described.

FIG. 5A is a block diagram illustrating the overall configuration of the information processing apparatus 100 according to the first embodiment. As illustrated in FIG. 5A, the information processing apparatus 100 includes a performance data storage unit 10, a statistical processing unit 20, a statistical data storage unit 30, a representative value calculation unit 40, a representative value data storage unit 50, and a data selection unit. It functions as 60, a KPI change amount calculation unit 70, a correlation degree storage unit 80, and the like.

FIG. 5B is a block diagram illustrating a hardware configuration of the information processing apparatus 100. As illustrated in FIG. 5B, the information processing device 100 includes a CPU 101, a RAM 102, a storage device 103, a display device 104, and the like. The CPU (Central Processing Unit) 101 is a central processing unit.

The CPU 101 includes one or more cores. The RAM (Random Access Memory) 102 is a volatile memory that temporarily stores a program executed by the CPU 101, data processed by the CPU 101, and the like. The storage device 103 is a non-volatile storage device. As the storage device 103, for example, a ROM (Read Only Memory), a solid state drive (SSD) such as a flash memory, a hard disk driven by a hard disk drive, or the like can be used. The storage device 103 stores the information processing program. The display device 104 is a liquid crystal display, an electroluminescence panel, or the like, and displays a calculation result or the like. In this embodiment, each part of the information processing apparatus 100 is realized by executing a program, but hardware such as a dedicated circuit may be used.

FIG. 6 is a diagram illustrating a flowchart showing an example of processing executed by the information processing apparatus 100. As illustrated in FIG. 6, the statistical processing unit 20 extracts production record data from the record data storage section 10 that stores various production record data of the production line (step S1).

Next, the statistical processing unit 20 categorizes the production record data by status, production apparatus, worker, and product type (step S2). The status is a production record parameter in this embodiment, and is MCT [min], stage change time [min], pause recovery time [min], pause occurrence frequency [times], number of resources [person], Consideration of work priority.

Next, the statistical processing unit 20 performs statistical processing for each category categorized in step S2 (step S3). For example, the statistical processing unit 20 calculates the maximum value max, the minimum value min, the average value ave, the standard deviation σ, and the like. The calculated statistical data is stored in the statistical data storage unit 30.

Next, the representative value calculation unit 40 calculates a representative value for each production actual parameter (step S4). The calculated representative value is stored in the representative value data storage unit 50. Representative values are mean, median, mode, and so on. In this embodiment, as an example, an average value is used as a representative value.

Next, the data selection unit 60 selects the production record data in the adoption direction with respect to the average value for each production record parameter based on the master table (step S5). Specifically, the data selection unit 60 selects only one of the production record data smaller than the average value and the production record data larger than the average value for each production record parameter.

FIG. 7A is a diagram illustrating a master table. For example, as illustrated in FIG. 7A, a plurality of types of parameters are set in the master table. As an example, the parameters include MCT average, MCT variation, step change average, step change time variation σ, pause recovery time average, pause recovery time variation, pause occurrence frequency, number of resources, work priority order consideration, etc. Includes status. The MCT average is an average value of MCT. The MCT variation is a statistic representing the variation such as the standard deviation of the MCT. The step change average is the average time required for each step change (step change time). The step change time variation σ is a statistic that represents variations such as the standard deviation of the step change time. The average pause recovery time is the average value of the time required to recover the pause. The number of resources is the number of surplus personnel such as supporters who perform temporary suspension restoration work and supporters who perform step change work.

The direction of improvement is defined for each parameter. For example, the smaller the MCT average, the better the production process. As for the number of resources, the larger the number, the better the production process. If the direction of improvement is "reduce", production performance data smaller than the average value will be selected. If the direction of improvement is "increase", production performance data larger than the average value will be selected. Regarding the MCT average and the MCT variation, the production actual data smaller than the average value is selected for the MCT [min] of the production actual parameter. For the step change average and the step change time variation σ, the production record data smaller than the average value is selected for the step change time [min] of the production record parameter. For the average pause recovery time and the variation in the pause recovery time, the production record data smaller than the average value is selected for the pause time [min] of the production record parameter.

As illustrated in FIG. 7B, production performance data from the minimum value (7 min) to the average value (8 min) is selected for the step change average. As illustrated in FIG. 7C, production record data from the minimum value (0 min) to the average value (1.5 min) is selected for the step change time variation σ.

FIG. 8 is a diagram illustrating production performance data selected for each adjustment parameter. In FIG. 8, the average value of MCT [min] of the actual production parameter is 11.1. Of each production record data, data having an MCT of less than 11.1 is selected. In the example of FIG. 8, two production numbers 1 and 3 are selected, and three production numbers 2, 4 and 5 are not selected. That is, the data used is suppressed to 2/5.

Next, the KPI change amount calculation unit 70 calculates the KPI change amount by changing the production record parameter using the selected production record data (step S6). Here, the slope (correlation degree) of the KPI change amount with respect to the change amount of the production actual parameter is calculated. For example, when assigning the value of the production actual parameter MCT [min], the value of the parameter (MCT average or MCT variation) stored in the master table of FIG. 7A may be assigned.

The calculated correlation degree is accumulated in the correlation degree accumulating unit 80. Next, the display device 104 displays the degree of correlation calculated in step S6 (step S7). By the above processing, the degree of correlation between each production record parameter and the KPI related to the production record can be calculated. The display device 104 may display only the production record parameters having the highest correlation degree, or may display the order of the correlation degree and the like.

According to this embodiment, the representative value calculation unit 40 calculates the representative value for each production record parameter with respect to the accumulated production record data. Based on the master table, the data selection unit 60 selects only one of the production record data smaller than the representative value calculated by the representative value calculation unit 40 and the production record data larger than the representative value calculated for each production record parameter. The KPI change amount calculation unit 70 calculates the degree of correlation with the KPI related to the production performance for each production performance parameter using the production performance data selected by the data selection unit 60. With this configuration, the number of production record data for calculating the degree of correlation with KPI can be reduced. In addition, since it becomes possible to grasp the magnitude of the correlation degree, it becomes possible to grasp which production performance parameter should be adjusted to greatly improve the KPI. If the production record data in the direction of improving the production process is selected and used as the adjustment parameter, it is not necessary to perform digital planning in the case where the production process is worse than the current situation.

In the above example, the representative value calculation unit 40 functions as an example of the calculation unit that calculates the representative value for each production record parameter with respect to the accumulated production record data. As an example of a selection unit in which the data selection unit 60 selects only one of production performance data smaller than the representative value calculated by the calculation unit and production performance data larger than the representative value calculated by the calculation unit for each production performance parameter based on the master table. Function. The KPI change amount calculation unit 70 functions as an example of a calculation unit that calculates the degree of correlation with the KPI related to the production performance for each production performance parameter using the production performance data selected by the selection unit.

Although the examples of the present invention have been described in detail above, the present invention is not limited to the specific examples, and various modifications and modifications are made within the scope of the gist of the present invention described in the claims. It can be changed.

10 Actual data storage unit 20 Statistical processing unit 30 Statistical data storage unit 40 Representative value calculation unit 50 Representative value data storage unit 60 Data selection unit 70 KPI change amount calculation unit 80 Correlation degree storage unit 100 Information processing device

Claims (5)

A calculation unit that calculates representative values for each production record parameter for the accumulated production record data,
Based on the master table, for each production record parameter, a selection unit that selects only one of production record data smaller than the representative value calculated by the calculation unit and production record data larger than the representative value,
An information processing device including a calculation unit that calculates a degree of correlation with a KPI related to a production record for each production record parameter using the production record data selected by the selection unit. The master table defines the direction for improving the production process.
The information processing apparatus according to claim 1, wherein the selection unit selects production record data in a direction in which the production process is improved. The information processing apparatus according to claim 1 or 2, wherein the representative value is an average value. For the accumulated production record data, the calculation unit calculates the representative value for each production record parameter.
Based on the master table, the selection unit selects only one of the production performance data smaller than the representative value calculated by the calculation unit and the production performance data larger than the representative value calculated for each production performance parameter.
An information processing method characterized in that, using the production record data selected by the selection unit, the calculation unit calculates the degree of correlation with the KPI related to the production record for each production record parameter. On the computer
For the accumulated production record data, the process of calculating the representative value for each production record parameter,
Based on the master table, for each of the production record parameters, a process of selecting only one of the production record data smaller than the calculated representative value and the production record data larger than the calculated representative value, and
An information processing program characterized by executing a process of calculating a degree of correlation with a KPI related to a production record for each of the production record parameters using the selected production record data.

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