The present application claims priority from Japanese application serial no. JP2007-179194, filed on Jul. 9, 2007, the content of which is hereby incorporated by reference into this application.
1. Field of the Invention
The invention relates to a production instruction system and a production instruction method creating production instructions having a high rate of strictly observing delivery time in a short period of time in production instruction operation of products in which a characteristic value of individual product does not satisfy a standard value aimed in design and completion volume is liable to fluctuate due to instability of production processes.
2. Description of Related Art
A production process of high-tech device products such as a hard disk and a liquid crystal display generally includes a parts forming process which requires microfabrication such as a thin-film process or polishing and an assembly process assembling completed parts into products. For example, concerning the hard disk, a magnetic head or a disk as primary parts are fabricated in different parts forming processes and plural magnetic heads or disks are assembled in the assembly process with other parts such as a spindle motor and a frame to complete products through a test process.
In related arts, there is a method of observing delivery time by instructing workers to perform production in consideration of divergence of actual performance with respect to completion requirement volume, priority of respective products, a load status of equipment and the like. For example, in JP-A-5-12298, there is a method of observing delivery time by grasping a lot arriving from a post-process equipment group to a present process equipment group to shorten lead time of a lot whose work priority is high. Further, for example, in JP-A-7-129672, there is a method of observing delivery time by a work instruction which allows actual work progress to follow target warehousing volume. On the other hand, in JP-A-10-161708, there is a method of observing delivery time by making good use of production capacity of the whole line by controlling start of work in respective equipment groups so as to satisfy buffer capacity constraints, making use of equipment capacity in a continuous process equipment group and planning a work start schedule based on an individual lot attribute.
There is also a method of observing delivery time by allocating customers according to standards or applications of products based on characteristics test results of products in the production process. Concerning an intermediate stock customer allocation system, for example, there are JP-A-11-353393 and JP-A-11-345750.
However, in the case of high-tech devices such as the hard disk and the liquid crystal display, there may be a case in which a characteristic value of each product does not satisfy a standard value which is aimed in design and a product will have another standard value whose characteristic value is in a rank lower than the original standard value, or the product may be defective due to instability of production processes. As a result, there arises a problem that the completion volume of the product fluctuates and diverges from a production plan to adversely affect the strict observance of delivery time for customers.
In the above related arts, such instability of production processes is not considered and it is difficult to provide production instructions for strictly observing delivery time for customers. As a result, there arises a problem that the delay of delivery time for customers occurs.
In view of the above problem, an object of the invention is to provide a production instruction system and a production instruction method providing production instructions in real time, in which variations of characteristic values and yield variations are calculated.
In order to solve the above problems, the present invention proposes a product instruction system which creates production instructions of a product in which a characteristic value of each product is liable to vary from a standard value aimed in design due to instability of production processes, including an information collection unit collecting performance information of results selected as a product in accordance with design when each product satisfies an original design standard value, selected as other products having different standard values when each product does not satisfy the original design standard value, or selected as detective products when each product does not satisfy any standard value according to measured characteristic values by measuring characteristic values of each inputted product in a test process in plural production processes, a statistical work calculation unit calculating the average number of performance and a standard deviation in an arbitrary period by using the collected performance information, a target achievement probability calculation unit calculating a target achievement probability to a final target in production performance at present time in accordance with information obtained from the information collection unit and the statistical work calculation unit, a production instruction change determination unit fixing the change of product mixes of all products, determining production instruction change by comparing the target achievement probability to the final target calculated in the target achievement probability calculation unit with a threshold set in advance, and repeating processing of changing the product mix until the target achievement probability becomes the threshold or more, and a production instruction creation unit creating production instructions based on the product mix information fixed by the production instruction change determination unit and production plan information, as well as a production instruction method executed by the system.
According to the invention, the divergence between the number of plans and the number of performance generated from variations of production volume due to variations of yield is prevented to the minimum, thereby creating production instructions for strictly observing delivery time for customers in a short period of time. The divergence between the number of plans and the number of performance is prevented to the minimum, thereby achieving improvement of the strict observation rate of delivery time and reduction of products in-process in the production line.
Hereinafter, an embodiment of the invention will be explained.
Next, the production instruction system according to the invention can be constructed on a computer system having a common configuration as shown in
The external storage device (303) of the production instruction system of the invention includes, for example, as shown in
An example of the input plan information (401) is shown in
An example of the yield information (402) is shown in
An example of the completion requirement volume information (403) is shown in
An example of the priority information (406) is shown in
Next, a processing program executed by the CPU (301) of the production instruction system according to the invention will be explained with reference to
In the invention, production performance information in a production process of each product is collected first (601). The details of processing based on the production performance information are shown in
Next, the number of inputs is calculated (1202) by aggregating data according to the input product name, the process and the date, targeting the production performance information by each individual identification number. Also, the completion performance is calculated (1203) by aggregating data according to the input product name, the product name after being inputted, the process and the date, targeting the product performance information by each individual identification number. Next, yield (=completion performance/number of inputs) is calculated (1204) by aggregating data according to the input product name, the product name after being inputted, the process and the date, targeting the product performance information by individual identification number unit. Lastly, data of the number of inputs, the completion performance and the yield is created as production performance aggregation information (1205) by aggregating data according to the input product name, the product name after being inputted, the process and the date.
An example of the production performance aggregation information is shown in
In addition, a corresponding period of the present processing is determined in advance. For example, the corresponding period is determined such as for a month in the past until the present day.
Next, calculation of statistical work is performed (602), which calculates the average number of performance and a standard deviation in an arbitrary period by using the collected performance information. For example, when the corresponding period is “n”, the yield of a product “i” is Yij, the average μi of the yield Yij of the product “i” is shown in a formula 1. The standard deviation of the yield Yij of the product “i” is shown in a formula 2.
Next, a target achievement probability α to a final target in the production performance at present time is calculated by the target achievement probability calculation unit based on the information obtained by the information collection unit and the statistical work calculation unit (603). For example, the target achievement probability α satisfies a formula 3.
In the above formula, the difference between the input plan and production performance in a certain point “t” (0=<t=<R) during a certain production period R of a certain product “i” is Xi, the average of the production performance of the certain product “i” is μ, the dispersion is σ, and the completion requirement volume in the production period R is Q. In addition, Z(1−α) indicates a value of an inverse function of the cumulative distribution function in the normal distribution when the target achievement probability is α.
A derivation process of the formula 3 is explained. When the production performance in the production period R is described as the normal distribution NR, the production performance at the certain point “t” can be defined as Nt. Also, the production plan at the certain point “t” can be defined as St, which can be described by a formula 4.
The difference Xt between the input plan and the production performance in the certain point “t” (0=<t=<R) during the production period R can be described by a formula 5.
The average μt and the dispersion at can be defined by a formula 6 and a formula 7 since both of them are the normal distribution.
Similarly, the average μt and the dispersion at of the production volume NR−t of residual time R−t can be defined by a formula 8 and a formula 9.
According to the above, when the target achievement probability is α, a probability (1−α) that the target is not achieved satisfies a formula 10.
Φ indicates a value of normal distribution. When the formula 10 is replaced with a value Z which is an inverse function of the cumulative distribution function in the normal distribution, a formula 11 is derived.
In the above formula, Z(1−α) satisfies a formula 12.
φ(Zα)=α [Formula 12]
Next, the target achievement probability α calculated in the step (603) in
The processing flow of
Next, statistical data of the product name is acquired (1302). Specifically, the data will be the yield average value and the yield standard deviation calculated in (602). In this case, the yield average value of 100 GB is 50% and the yield standard deviation is 30%. Next, the product name having high priority in the kinds of input products with respect to the completed products is acquired (1303). For example, when the completed product is 100 GB, the input product is only 100 GB, however, when the completed product is 80 GB, there are two kinds of input products which are 100 GB and 80 GB. In this case, the priority as shown in
Then, the target achievement probability α to the final target in the production performance until the present time is calculated (1304). The target achievement probability α satisfies the formula 3. In the case of 100 GB, calculation is performed under the condition that a certain product i=1, a certain point t=4 in a production period (operating time per day) R=8. The difference Xi between the input plan and the production performance in the point “t” of the production period R is expressed by the following formula:
In the above formula, the planned number of inputs of the product “i” per day is Ii, the production performance of the product “i” at the point “t” in the production period R is Ci,t. Also, the average of the production performance of 100 GB is μ=100, the dispersion is σ=30, the completion requirement volume in the production period R=8 is Q=100. The target achievement probability α at this time is 41%.
Next, whether all kinds of input products have been completed is confirmed (1305). In the case of 100 GB, since the kind of the input product is only one, the process ends. In the case that there are two kinds or more of input products as in the case of 80 GB, the process returns to (1303) step, and the product name having the secondarily high priority in the input kinds is acquired, calculating the target achievement probability again (1304).
When all kinds of input products have been completed, whether the target achievement probability is the threshold Thi or more is confirmed (1306). The threshold is usually 50%. In the case that the priority of the product is extremely high and the delivery date is strict, the threshold Thi is increased. In the case of 100 GB, when the threshold Thi is 50%, the target achievement probability αi is 41%, therefore, the product mix Pi will be changed (1307).
When the target achievement probability αi is less than the threshold, the change of the product mix Pi is performed by a formula 14.
P′
i
=P
i(1+(Thi−Xi)) [Formula 14]
The product mix Pi is shown by the following formula (formula 15):
In the above formula, the planned number of inputs of the product “i” per day is Ii, the number of products is “n”.
In the case of 100 GB, for example, according to the input plan information of April 25 in
In the above, the planned number of inputs of the product “i” is Ii, the number of products is “n”.
As described above, after the product mix P′i is changed, the process proceeds to step (1302), the target achievement probability αi is calculated again, repeating the process until becoming the threshold or more. In the example of 100 GB, the target achievement probability αi calculated again is 49%, therefore, the product mix is changed again. In the third calculation, the probability exceeds 50%, the product mix is fixed here (604).
The above processing is repeated until all products are completed (1308).
Lastly, the product instruction is created based on the changed product mix and the input plan information in the external storage device (303) is updated (605).
According to the processing flow shown above, the production instruction can be created in real time, in which divergence between the planned number of inputs and the number of completion generated by variations of the production volume is prevented to the minimum so as to strictly observe delivery time for customers.
Number | Date | Country | Kind |
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2007-179194 | Jul 2007 | JP | national |