JP2012181626A - Production planning system and production planning method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a production planning system and a production planning method for making a production plan to minimize the whole costs even under such situations that the delay of a term of delivery from a supplier or the trouble of quality incompatibility is continuously generated.SOLUTION: A computer optimizes the date of delivery of parts to minimize the expectation value of the whole costs of a production process on the basis of costs to be spent when parts to be used in a production process including a plurality of operation processes are delivered before a term of delivery in a standard schedule and costs to be spent when the parts are delivered after the term of delivery in the standard schedule.

Description

  The present invention relates to a production planning system and a production planning method for optimizing the delivery date of parts in a production process.

  For example, when manufacturing products (medium-volume products) that produce about 1 to 10 pieces per day at the manufacturer, most of the parts used in these products are purchased from suppliers and are occasionally It is funded by being delivered.

  However, there are cases where parts are not delivered from the supplier at the designated delivery date, or even if the parts are delivered at the designated delivery date, the quality of the parts is incompatible. As a cause of this, in general, a manufacturer must sufficiently manage the delivery date, quality, etc. of each of the huge parts in order to distribute various parts to multiple suppliers and place an order. Is not possible. In addition, each supplier may have insufficient production management.

  A specific example will be described with reference to FIG. As shown in FIG. 5, a product is manufactured through steps A and B. Process A requires part 1, process B requires part 2, and the process A is already completed before entering process B. As shown in the upper side of FIG. 5, as part of the initial plan, as soon as the part 1 is delivered, the process A is performed, the part 2 is delivered in accordance with the end time of the process A, and the part 2 is delivered. It is assumed that a plan is made so that the operation of the process B is started immediately. Here, as shown in the lower side of FIG. 5, when the delivery date of the component 1 is delayed from the supplier, the period until the process A is completed is delayed, so the period for starting the process B is also delayed. . For this reason, the cost for storing the components 2 for use in the process B, which are delivered on time, is generated. Furthermore, the product completion date is also delayed by the amount that the delivery date of the part 1 is delayed.

  In this way, in the situation where delays in delivery from suppliers and troubles of non-conformity are continuously occurring, on the premise that such troubles occur, there is a margin in delivery time or there is a lot of inventory It is considered effective to make a production plan such as

  Patent Document 1 discloses a production management system and a production management method that realize a reduction in lead time from ordering to delivery date for a product composed of a plurality of parts and arrangement without waste for the parts.

  Patent Document 2 discloses a supply chain simulation system that can simulate logistics and commercial operations and quantitatively evaluate the supply chain plan based on inventory, cost, profit, working capital, cash flow, etc. Has been.

JP 2002-140110 A JP 2002-145421 A

  However, the production management method disclosed in Patent Document 1 described above only considers fluctuations in demand from customers as a risk in production planning. In the situation where it occurs continuously, we do not have production plans such as allowance for delivery time or having a large inventory.

  Further, the system disclosed in Patent Document 2 is intended to evaluate the entire supply chain, and therefore does not draft a production plan.

  The present invention has been made to solve the above-described problem, and is a production that minimizes the overall cost even in the situation where delays in delivery from suppliers and troubles of non-conformity are continuously occurring. A production planning system and a production planning method capable of formulating a plan are provided.

In order to solve the above problems, the present invention employs the following means.
According to a first aspect of the present invention, there is a cost required when parts used in a production process including a plurality of work processes are procured with respect to a delivery date in a standard schedule, and the parts are in the standard schedule. The computer optimizes the delivery date of the parts so that the expected value of the total cost of the production process is minimized based on the cost when procurement is delayed with respect to the delivery date. This is a production planning system.

  According to the production planning system according to the first aspect of the present invention, the computer optimizes the delivery date of the parts so that the expected value of the overall cost of the production process is minimized, so that the delivery time of the parts is delayed. Even in this case, the delay can be prevented from affecting the downstream process and the final product completion date as much as possible.

  The production planning system according to the first aspect of the present invention includes means for creating a standard schedule based on the context of each work process and the parts used in each work process, and means for calculating the overall cost. And means for optimizing the delivery date so as to minimize the overall cost.

  According to this configuration, the delivery date of the part is optimized so that the expected value of the overall cost of the production process is minimized, so even if the delivery date of the part is delayed, the delay is as low as possible in the downstream process and the final process. The product completion date can be prevented from being affected.

  The second aspect of the present invention relates to the cost required when parts used in a production process including a plurality of work processes are procured ahead of the delivery date in the standard schedule, and the parts are in the standard schedule. The computer optimizes the delivery date of the parts so that the expected value of the total cost of the production process is minimized based on the cost when procurement is delayed with respect to the delivery date. This is a production planning method.

  According to the present invention, it is possible to devise a production plan that minimizes the overall cost even in a situation where delays in delivery from a supplier and troubles of non-conformity in quality occur continuously.

It is a block diagram which shows schematic structure of the production planning system which concerns on embodiment of this invention. It is a figure which shows the structure of the process part of the production planning system which concerns on embodiment of this invention. It is a figure which shows the flowchart of the production planning system which concerns on embodiment of this invention. It is a figure which shows the flowchart of the optimization process in the production planning system which concerns on embodiment of this invention. It is a figure explaining the conventional production planning method. It is a figure which shows an example of the data stored in the database in embodiment of this invention, (a) is storage cost DB, (b) is process data DB, (c) is schedule data DB, (d) is process * Parts relation DB, (e) is completion condition DB, (f) is risk data DB, (g) is inter-process margin data DB, (h) is inter-process context DB, (i) is initial schedule data DB. FIG. It is a figure explaining the initial schedule data in embodiment of this invention. In an embodiment of the present invention, it is a figure explaining a production plan when the delivery date of parts # 1 is advanced one day. It is a figure explaining the example of the total cost calculated in embodiment of this invention.

Embodiments of a production planning system according to the present invention will be described below with reference to the drawings.
FIG. 1 is a block diagram showing a schematic configuration of a production planning system according to an embodiment of the present invention. As shown in FIG. 1, an R shape measuring apparatus according to the present embodiment includes a CPU (Central Processing Unit) 1, a main storage device 2 such as a RAM (Random Access Memory), and an auxiliary device such as an HDD (Hard Disk Drive). The storage device 3 includes an input device 4 such as a keyboard and a mouse, and an output device 5 such as a monitor and a printer.

  Various programs and various databases are stored in the auxiliary storage device 3. The CPU 1 reads out the programs from the auxiliary storage device 3 to the main storage device 2 such as a RAM and executes them, thereby realizing various processes.

  As shown in FIG. 2, these processes include a standard schedule creation unit (standard schedule creation unit) 12, an overall cost calculation unit (overall cost calculation unit) 13, an optimization processing unit (optimization unit) 14, and a completion. It includes processes executed by the condition setting unit 15 and the result display unit 16, and these processes are controlled by the overall control unit 11. The overall control unit is configured such that the user can execute each process independently via, for example, a user interface.

  The various processes described above are performed by exchanging data with various databases (DB) in the data storage unit 17 as necessary. The data storage device 17 includes a storage cost DB 171, process data DB 172, schedule data DB 173, process / part relation DB 174, completion condition DB 175, risk data DB 176, inter-process margin data DB 177, inter-process context relation 178, and initial schedule data DB 179. Etc. are included.

  In the storage cost DB 171, as shown in FIG. 6A, costs for storing parts delivered from a supplier in a warehouse or the like for a certain period are stored corresponding to each part.

  In the process data DB 172, as shown in FIG. 6B, the number of days required from the start to the end of each process is stored for each process.

  As shown in FIG. 6C, the schedule data DB 173 stores schedule data that minimizes the overall cost after the optimization process is performed for each of the large schedules. A large schedule means a process including a plurality of processes.

  In the process / part relation DB 174, as shown in FIG. 6D, parts used in each process are stored for each process.

In the completion condition DB 175, as shown in FIG. 6E, the completion condition for executing the optimization process is stored.
As shown in FIG. 6F, the risk data DB 176 stores risk data corresponding to each component. In this embodiment, a delivery delay probability is adopted as the risk data.

  In the inter-process margin data DB 177, as shown in FIG. 6 (g), it is set how many margin days are provided from the end of the previous process to the start of the next process, or from the part delivery date to the start of the next process. Margin data is stored.

  In the inter-process context DB 178, as shown in FIG. 6H, information on the context is stored when a work order is predetermined between the processes. For example, a certain process and a process that needs to be performed prior to performing the process are stored correspondingly.

  In the initial schedule data DB 179, as shown in FIG. 6 (i), information on critical paths and information on which processes each large process includes when the margin date between processes is set to zero is stored. The

  As described above, each process is configured such that the user can execute each process independently via the user interface. Generally, however, the production plan is determined according to the flowchart shown in FIG. Done.

Hereinafter, the production planning method shown in FIG. 3 will be described in detail with reference to the drawings.
First, an initial schedule is created (step SA1). The initial schedule creation process is executed by the standard schedule creation unit 12. The standard schedule creation unit 12 includes which process data stored in the data storage unit 17, inter-process context data, process / parts relationship data, inter-process margin data, and each large process stored in the initial schedule data DB 179. Is included. In creating the initial schedule, the critical path of a large process is calculated with the inter-process margin data set to zero, so the stored inter-process margin data is not used. The calculated critical path of the large process is stored in the initial schedule data DB 179.

  For simplicity, the large process I in the specific example shown in FIGS. 6 (a) to (i) will be described. By referring to the initial schedule data DB shown in FIG. 6 (i), it is understood that the large process I includes the processes A and B. By referring to the process / part relation DB shown in FIG. 6D, the processes A and B can be understood that the process A requires the part # 1, and the process B requires the part # 2. . Further, by referring to the process data DB shown in FIG. 6B, it can be seen that the number of days required for the process A is 10 days and the number of days required for the process B is 14 days. Then, by referring to the inter-process context DB shown in FIG. 6 (h), it is understood that the preceding process of the process B is the process A. In this way, the initial schedule of the large process I is created as shown in FIG.

  Next, based on the calculated initial schedule, the total cost in the initial schedule is calculated (step SA2). Various indicators can be adopted as the total cost. However, in this embodiment, an additional cost when the parts are delivered in advance (an advance cost) and a time when the final product is delayed. The total cost is determined by adding the additional cost (delay completion cost). As the advance cost, for example, a storage cost such as a warehouse cost required for storing in a warehouse from when a part is delivered until it is actually used in a process can be considered. Possible delay costs include the penalty to be paid to the customer if the product is delayed and the delivery to the customer delayed, or the additional cost incurred when the downstream process is delayed due to this process being delayed. .

  Here, for the sake of simplicity, the relationship between the delivery date of the part # 1 and the total cost will be specifically described. The penalty paid to the customer is calculated as 100 per day.

  For example, when a production plan as shown in FIG. 7 is made, it can be seen that the probability that the part # 1 is delivered on time is 80% when the risk data DB is referred to. The advance cost is calculated as the product of the storage cost and the number of days that need to be stored. In FIG. 6 (f), since the probability that the parts are delivered before the delivery date is 0%, storage is not required, and the advance cost is zero. On the other hand, the completion delay cost is calculated by the product of the penalty and the probability that the product completion is delayed. Assuming that part # 1 is delivered on time with a probability of 80%, part # 1 has a delivery time delay with a probability of 20%, resulting in a delay in product completion. Therefore, the completion delay cost is 20. Since the advance cost is zero, the total cost is 20.

  Next, completion conditions are set (step SA3). The completion condition is a condition for completing the process of the next optimization process (step SA4). Specifically, as shown in FIG. 6E, a preset calculation time, an evaluation value, the number of processing loops, or a combination of these is employed.

  Next, the optimization process (step SA4) is executed using the completion condition set in step SA4.

The optimization process will be specifically described with reference to FIG.
First, schedule data is read (step SB1). In the present embodiment, initial schedule data as shown in FIG. 7 is read.

  In the next conditional branch (step SB2), it is determined whether or not the calculated total cost satisfies the completion condition, that is, whether or not the total cost is an optimal solution. If the completion condition is satisfied, the process ends here. If the completion condition is not satisfied, the next inter-process margin data is changed (step SB3).

  After determining the margin data between processes, that is, how much the delivery date of the parts is advanced, the schedule of the entire process is recalculated (step SB4). For example, FIG. 8 shows a schedule when the delivery date of the part # 1 is planned one day ahead.

  Based on the schedule recalculated in step SB4, the total cost is recalculated (step SB5).

In the next conditional branch (step SB6), it is determined whether or not the total cost calculated in step SB5 is smaller than the total cost stored as the optimum value. When the value is small, the total cost is updated as the optimized total cost, the schedule data is updated (step SB7), the process returns to step SB2, and the process is repeated until the completion condition is satisfied. If the value has not decreased, the process returns to step SB2 without updating the optimized overall cost and schedule data, and the process is repeated until the completion condition is satisfied.
Finally, the result is displayed (step SA5).

  In the case of the present embodiment, in the loop processing in this optimization processing, the total cost as shown in FIG. 9 is calculated according to the number of days ahead of the delivery date of the part # 1. As a result, it can be seen that the total cost is the smallest when the delivery date of the part # 1 is advanced two days.

  In the present embodiment, the relationship between the delivery date of the part # 1 and the total cost has been described for the sake of simplicity. However, in the actual site, the part is supplied from a plurality of different suppliers or manufactured in-house. The event is even more complicated. Therefore, in the optimization process, general techniques for solving various optimization problems such as artificial intelligence, fuzzy sets, neural networks, genetic algorithms, and the like can be used.

  Although the embodiments of the present invention have been described in detail with reference to the drawings, the specific configuration is not limited to these embodiments, and includes design changes and the like within a scope that does not depart from the gist of the present invention. .

1 CPU
2 Main storage device 3 Auxiliary storage device 4 Input device 5 Output device 11 Overall control unit 12 Standard schedule creation unit 13 Overall cost calculation unit 14 Optimization processing unit 15 Completion condition setting unit 16 Result display unit 17 Data storage unit

Claims (3)

  Costs when parts used in production processes including multiple work processes are procured ahead of the delivery date on the standard schedule, and the parts are procured with a delay from the delivery date on the standard schedule. A production planning system, wherein the computer optimizes the delivery date of the parts so that the expected value of the total cost of the production process is minimized based on the cost of the production.   Means for creating a standard schedule based on the context of each work process and the parts used in each work process, means for calculating the overall cost, and the delivery date so as to minimize the overall cost The production planning system according to claim 1, comprising means for optimizing. Costs when parts used in production processes including multiple work processes are procured ahead of the delivery date on the standard schedule, and the parts are procured with a delay from the delivery date on the standard schedule. A production planning method, wherein the computer optimizes the delivery date of the parts so that the expected value of the overall cost of the production process is minimized based on the cost of the production.

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