JPH05342220A - Process plan evaluating system - Google Patents

Abstract

PURPOSE:To provide a process plan evaluating system which can quantatively evaluate a process plan by calculating the stock quantity and the sold-out rate of a product in term of cost. CONSTITUTION:This system is provided with a process plan preparing part 1 preparing manufacture schedule data, order reception predicting data and stock predicting data, a pattern storage part 4 assigning plural order reception predicting patterns by the unit of a month to each month within a prescribed period at every product number and storing it and a by-date/by-product-number order reception predicting part 3 assigning the order reception predicting quantity of optional product number and optional month to each day in accordance with the order reception predicting pattern assigned to a corresponding product number and a corresponding month. The system is also providing with a simulation part 5 simulating the stock quantity and the sold-out rate from order reception predicting data of the corresponding product number and the corresponding month assigned by date and manufacture schedule data of the corresponding product number and corresponding month, and a cost calculation part 6 calculating the stock quantity and the sold-out rate obtained by the simulation part 5 in term of the cost in accordance with a prescribed expression.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION The present invention makes it possible to create an optimal daily production plan and process plan by simulating changes in the inventory quantity and out-of-stock rate of products produced by the advanced production method. Process planning evaluation system.

[0002]

2. Description of the Related Art When a product with large fluctuations in shipment (for example, a seasonal product that often appears in early spring but rarely appears after summer) is planned in advance and produced in advance when the shipment volume is small, and inventories. In order to increase the operating rate of the manufacturing equipment, the accelerated production method has been used conventionally.

[0003]

However, in the above-mentioned conventional advance production method, since the subsequent production is carried out based on the original plan, the shipping record is largely deviated from the shipping plan, and an unexpected accident or the like occurs. When the manufacturing equipment could not be operated in, there was a problem that out-of-stock and overstock were generated. In addition, in order to avoid out-of-stock due to an increase in shipping volume in the front-running production method, the average inventory quantity must be set large,
There was also a problem that it was not good for inventory management.

Therefore, recently, there has been developed a system for automatically drafting a process plan by a calculation logic which achieves reduction of excess inventory and reduction of out-of-stock rate.

However, even in the process planning made by such a conventional system, the result of actual process allocation can always achieve the theoretical inventory amount and out-of-stock rate due to machine constraints, setup change constraints, and the like. Not necessarily.

The present invention has been made in view of the above circumstances, and an object thereof is to predict changes in the stock amount and out-of-stock rate from a planned process plan and order forecast, and convert the cost into a predetermined calculation formula. Therefore, it is to provide a process plan evaluation system capable of quantitatively evaluating a process plan.

[0007]

[Means for Solving the Problems] In order to solve the above problems, the process plan evaluation system of the present invention uses the production plan data of the current year based on the production results of the previous year and the orders of the current year based on the order results of the previous year. A process plan creation section that creates forecast data and inventory forecast data for the current year from the production plan data and the order forecast data, and each data created in the process plan creation section by an operator's input operation. And an input section for correcting the order forecast pattern for each month, and a pattern storage section for allocating and storing the forecast order patterns for each product number in each month within a predetermined period, and the process plan creation. The forecasted quantity of orders for any month for any product number created by the department is assigned to the corresponding month for the product number stored in the pattern storage unit. It is created by the daily / part number-specific order forecasting section that allocates by day according to the order forecasting pattern, the order forecast data for the month of the part number assigned by this day / part number-specific order forecasting section, and the process plan creation section. A simulation unit that simulates the stock amount and out-of-stock rate from the production plan data for the relevant month of the relevant product number, and a cost conversion unit that converts the inventory amount and out-of-stock rate obtained by this simulation unit into costs according to a predetermined calculation formula. A display unit that displays the cost calculated by the cost conversion unit as an evaluation value.

[0008]

[Operation] In the process plan preparation section, the production plan data of this year based on the production result of the previous year and the order forecast data of this year based on the order record of the previous year are produced, and the production plan data and the order forecast data are created. From this, inventory forecast data for this year will be created.

In the pattern storage unit, a plurality of monthly order forecast patterns are prepared, and these order forecast patterns are stored for each product number by being assigned to each month within a predetermined period (for example, the first half and the second half). There is. A plurality of types of order forecast patterns are created by the operator with reference to the actual order patterns of each product number in each month.

The daily-by-product / order-by-product-order order forecasting unit allocates the forecasted order quantity for the given product number for the given month, which is created by the process plan creation unit, to the relevant month of the product number stored in the pattern storage unit. Allocate by day according to the order forecast pattern. For example, 1st of the corresponding month of product number A is 5t, 2nd is 7t, 3rd is 8t, and so on.

[0011] The simulation section uses the order forecast data of the relevant month of the relevant product number assigned by the daily / product number order forecasting section and the production plan data of the relevant month of the relevant product number obtained by the process plan preparing section to make an inventory. Simulate the quantity and out-of-stock rate.

The cost conversion unit converts the inventory quantity and the out-of-stock rate obtained by the simulation unit into costs according to a predetermined calculation formula, and displays the result as an evaluation value on the display unit. The operator views the evaluation value displayed on the display unit, operates the input unit if necessary, and corrects the process plan created by the process plan creation unit. Then, again looking at the new evaluation value displayed on the display unit through the above processing procedure,
The process plan will be evaluated again.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing the electrical construction of the process plan evaluation system of the present invention.

In the figure, the production plan data for this year,
The process plan creation unit 1 that creates the order forecast data and the inventory forecast data is guided to the output of the data input unit 2 that inputs the various data while correcting the created process plan. The output of the creating unit 1 is guided to the daily / part number-specific order forecasting unit 3 and the simulation unit 5. Further, the output of the pattern storage unit 4 is guided to the order forecasting unit 3 for each day / product number. The output of the simulation unit 5 is guided to the cost conversion unit 6, and the output of the cost conversion unit 6 is guided to the display unit 7.

In the pattern storage unit 4, a plurality of prepared monthly order forecast patterns are stored by being assigned to each month within a predetermined period for each product number. In this example,
It is assumed that three order forecast patterns shown in FIG. 2 are prepared as the order patterns. In other words, the X pattern is a so-called slack pattern in which there are orders at the same pace in the beginning and the end and there are no orders in the middle. The Z pattern is a pattern in which orders are gradually increased from early, mid, and early.

Such three order forecast patterns are assigned to each month within a predetermined period of each product number. For example,
As shown in FIG. 3, an X pattern is assigned to each of the first half and the second half of the product number A, and a Y pattern is assigned to each month of the first half of the product number B and a Z pattern is assigned to each month of the second half. It is assigned. The pattern storage unit 4 stores an order forecast pattern assigned to each product number as shown in FIG. However, in FIG. 3, the pattern allocation is divided into the first half and the second half, but of course, the pattern allocation may be finely made every month.

The day-by-day / part number-specific order forecasting unit 3 sets the forecast order quantity of the arbitrary item number created by the process plan creating unit 1 in the month corresponding to the item number stored in the pattern storage unit 4. Allocate by day according to the assigned order forecast pattern. In addition, the simulation unit 5
From the order forecast data of the relevant month of the relevant product number assigned by the product number order forecasting unit 3 and the production plan data of the relevant month of the relevant product number obtained by the process plan creation unit 1, the stock amount,
Simulate out-of-stock rate.

The cost conversion unit 6 converts the inventory quantity and the out-of-stock rate obtained by the simulation unit into costs according to a predetermined calculation formula. The display unit 7 displays the cost calculated by the cost conversion unit 6 as an evaluation value. Further, the display unit 7 is adapted to display the transition of the stock amount and the out-of-stock rate simulated by the simulation unit 5 in a graph.

The process plan preparing section 1 prepares the production plan data of this year based on the production result of the previous year and the order forecast data of this year based on the order record of the previous year, and the produced production plan data and order forecast. Inventory data for this year is created from the data. The configuration of the process plan creation unit 1 and the procedure for creating each data will be specifically described below.

FIG. 9 shows a more specific structure of the process plan creation unit 1.

When roughly classified, the process plan preparing unit 1 forecasts future orders from the order forecast data of this year based on the order achievements of the previous year and the order achievement data of the past several months based on the order achievements of this year. The order forecasting means 11, the production forecasting means 12 for forecasting future production from the production plan data of this year based on the production performance of the previous year and the production performance data for the past several months based on the production performance of this year, and this production The stock forecasting means 13 performs a stock forecast in the future by subtracting the cumulative future order forecast predicted by the order forecasting means 11 from the cumulative future production forecast predicted by the forecasting means 12.

The order forecasting means 11 stores the order forecast data for this year based on the order record of the previous year for each product number.
Storage unit 111, a second storage unit 112 that stores the actual order data for this year for each product number, a coefficient calculation unit 113 that calculates a coefficient from the predicted order data and the actual order data,
The order forecasting processing unit 114 that performs the order forecasting process is provided. The first storage unit 111, the second storage unit 112, the coefficient calculation unit 113, and the order forecasting processing unit 114 are bidirectional. It is connected. Further, the second storage unit 112 is provided with order record data for each product number every month.

The production predicting means 12 stores the production plan data of this year based on the production results of the previous year for each product number.
Storage unit 121, a fourth storage unit 122 that stores the production performance data of this year for each product number, a coefficient calculation unit 123 that calculates a coefficient from the production plan data and the production performance data,
The production prediction processing unit 124 that performs the production prediction process, and the third storage unit 121, the fourth storage unit 122, the coefficient calculation unit 123, and the production prediction processing unit 124 are bidirectional. It is connected. Further, the fourth storage unit 122 is provided with production performance data for each product number every month.

The stock forecasting means 13 is an order forecasting processing section 11
A fifth storage unit 131 for storing the future order forecast data given from No. 4 for each product number; and a sixth storage unit 132 for storing the future production forecast data given by the production prediction processing unit 124 for each product number; It is composed of an inventory prediction processing unit 133 that performs future inventory prediction processing based on each of these data, and includes a fifth storage unit 131 and a sixth storage unit 13.
2 and the inventory prediction processing unit 133 are bidirectionally connected. In addition, the inventory prediction processing unit 133 has 1
Actual inventory data for each product number is given every month.

Then, the output of the inventory prediction processing unit 133 is
The configuration is such that it is led to the output means 14 provided with a display unit such as a CRT or a printer, or the like.

Next, the operation of the process plan creation unit 1 having the above configuration will be described with reference to each data shown in FIGS. However, here, only one product number (for example, C) will be described.

The first storage unit 111 of the order forecasting means 11 stores the order forecast data for the current year of the product number C shown in FIG. This order forecast data is part number C of the previous year.
It is created based on the actual orders received. That is, in the simplest case, the order receipt data of the product number C of the previous year is used as it is as the order forecast data of the product number C of the current year. However, it is also possible to use the order forecast data of this year that is obtained by modifying the order record of the previous year by adding some condition (for example, weighting).

The second storage unit 1 of the order forecasting means 11
12, the order record data of the product number C up to the present year shown in FIG. 11 is stored. In addition, the second storage unit 1
12 is given the order record data of the product number C of the elapsed month when one month has passed, and sequentially written in a predetermined area. However, in this embodiment, the present is set to September.

The order forecasting processor 114 reads out the data for three months from July to September in the order forecasting data of the product number C stored in the first memory 111, and uses it as a coefficient calculator. Output to 113. In addition, the second storage unit 1
7 in the order record data of product number C stored in 12
The data for three months from the month to September is read out and output to the coefficient calculation unit 113.

In the coefficient calculation unit 113, the first storage unit 11
Total of data read from 1 (50 + 90 + 60 =
200) and the total (50 + 125 + 75 = 250) of the data read from the second storage unit 112, and then the calculation of 250/200 is performed to obtain the coefficient (1.25).

Next, the order forecast processing unit 114 reads each data from October of the order forecast data of the product number C stored in the first storage unit 111, and obtains this data in the coefficient calculation unit 13. Multiplied by the coefficient (1.25)
New order forecast data for product number C after October shown in FIG. 12 is obtained. The new order forecast data is output to the inventory forecast processing unit 133 and stored in the fifth storage unit 131.

On the other hand, the third storage section 1 of the production predicting means 12
21 stores the production plan data of the product number C shown in FIG. 13 for this year. This production plan data is created based on the production record of the product number C in the previous year. That is, in the simplest case, the production record of the product number C of the previous year is directly used as the production plan data of the product number C of this year. However, it is also possible to use the production plan data for the current year by modifying the production performance of the previous year by adding some condition (for example, weighting).

The fourth storage section 1 of the production predicting means 12
22 stores the production performance data of the product number C up to the present year shown in FIG. The production result data of the elapsed month is given to the fourth storage unit 122 when one month has passed, and is sequentially written in a predetermined area. However, in this embodiment, the present is set to September.

The production prediction processing unit 124 reads out the data for three months from July to September in the production plan data of the product number C stored in the third storage unit 121, and uses this as the coefficient calculation unit. Output to 123. In addition, the fourth storage unit 1
7 in the production performance data of product number C stored in No. 22
The data for the three months from the month to September are read and output to the coefficient calculation unit 123.

In the coefficient calculation unit 123, the third storage unit 12
Total of data read from 1 (120 + 180 + 2
00 = 500) and the total of the data read from the fourth storage unit 122 (120 + 150 + 130 = 400), and then 400/500 is calculated to obtain the coefficient (0.
8) is calculated.

Next, the production prediction processing unit 124 reads each data after October in the production plan data of the product number C stored in the third storage unit 121, and obtains this data in the coefficient calculation unit 123. Multiplied by the coefficient (0.8)
The production plan data for a new part number C after October shown in FIG. 15 is obtained. The new production planning data is output to the inventory prediction processing unit 133 and stored in the sixth storage unit 132.

Based on the order forecast data of the product number C stored in the fifth storage unit 131 and the production plan data of the product number C stored in the sixth storage unit 132, the inventory prediction processing unit 133 will start from October. The stock forecast of the product number C will be performed.

The inventory prediction processing unit 133 is provided with the actual inventory data of the product number C as of the end of September. The actual inventory amount as of the end of September can be obtained by subtracting the accumulated order result data shown in FIG. 11 from the accumulated production result data shown in FIG. 14, and is 210 in this embodiment. However, the inventory at the end of March of this year is set to 0.

Therefore, the inventory forecast for October is

[0041]

[Equation 1] 210 (actual inventory amount) + 120 (production forecast) -2
00 (shipping forecast) = 130.

The inventory forecast for November is as follows:

[0043]

[Equation 2] 130 (stock forecast) +80 (production forecast) -22
5 (shipping forecast) =-15.

By sequentially performing the above calculation, the stock forecast data of the product number C shown in FIG. 16 can be obtained.

This inventory forecast data is output to the output means 4.

Further, the stock forecast processing unit 133 stores the forecast order data of the product number C of the previous year stored in the first storage unit 111 and the product number C of the previous year stored in the third storage unit 121. And the production plan data of the above are given via the order forecast processing unit 114 and the production forecast processing unit 124, respectively. The stock forecast processing unit 133 obtains the transition of the stock performance of the product number C of the previous year by subtracting the cumulative of the order forecast data of the previous year from the cumulative value of the production plan data of the previous year. The data is also output to the output means 14.

When the output means 14 is equipped with a display section such as a CRT, the output means 14 graphically displays the inventory forecast data and the actual inventory data of the previous year on the CRT screen.

FIG. 17 shows each data displayed as a graph on the CRT screen.

In the figure, the line graph shown by the solid line is the inventory plan data of this year based on the inventory performance of the previous year,
The line graph shown by the broken line is the actual inventory data until September of this year, and the line graph shown by the one-dot chain line following this line graph shown by the broken line is the stock forecast transition data after October obtained by the above stock forecast.

Inventory prediction data shown in FIG. 16 and FIG.
As can be seen from the line graph of the inventory forecast change shown in, the production plan and order forecast for October and later are revised based on the data for the past three months, with September as the present. It turns out that is turned negative. As a result, it is possible to modify the production plan so as to increase the production.

Here, as one method for avoiding a negative inventory, the inventory transition data from October of the previous year's inventory actual data indicated by the solid line is the same as that of this year's 10.
A possible method is to move the items in parallel so that the inventory changes after the month and modify the production plan. The inventory forecast transition data after this correction is shown by a two-dot chain line. Such correction of inventory transition data can be appropriately performed on the CRT screen.

Although the process plan preparing section 1 uses both the production forecast and the order forecast for future inventory forecast,
The production forecast may be as planned, and only the order forecast may be used. Further, although the production forecast and the order forecast are made based on the data for the past three months from the present, the number of months going back to the past is not limited to three months.

Next, the process plan data for each product number (production plan data) created by the process plan creating unit 1 and the inventory data for each product number are given to the order forecasting unit 3 for each day / product number. By doing so, the forecasted order quantity for each product number by day is calculated.

For example, the order forecasting section 3 for each day / part number is provided with an arbitrary month shown in FIG. 6 (specifically, 1 if the current month is September).
Order forecast data for each product number (for October) is given from the process plan creation unit 1. The order forecasting section 3 by day / product number stores this order forecasting data (A: 100, B: 50) in the pattern storing section 4 as shown in FIG. If so, the order forecast is calculated for each product number and day in the relevant month by allocating by day according to the order pattern in the second half of each product number. An example of this calculation result is shown in FIG.

In FIG. 7, the product number A is 5t on the first day of the relevant month.
Minutes, 7th on 2nd, 8t on 3rd ... 7t on 31st, and part number B is 2t on 1st of the relevant month.
Orders for 2t on 2nd, 2t on 3rd ... 3t on 31st are allocated.

The order forecast data thus assigned is given to the simulation section. Further, it is assumed that the simulation unit 5 is provided with the process plan data for each product number shown in FIG. 4 and the inventory data for each product number shown in FIG.

The simulation section 5 carries out a stock or out-of-stock simulation of all product numbers or designated product numbers based on these data.

FIG. 8 shows changes in the stock and out-of-stock rate of, for example, the product number A simulated in this way for the corresponding month. In the transition shown in FIG. 8, the product is out of stock from 18th to 20th.

Such transition data of the stock and the out-of-stock rate are given to the cost conversion section 6 and the display section 7 and displayed in a graph.

On the basis of the transition data of the inventory and the out-of-stock rate, the cost conversion unit 6 calculates the cost of holding the inventory and the cost of loss of sales opportunity due to the out-of-stock, and sums them to obtain the evaluation value of the process plan.

Here, the cost for holding the inventory is calculated by using the following equation, assuming that the cost is 20% of the selling price.

[0062]

[Equation 3] Daily inventory cost = Selling price per piece of the relevant product number x 0.2 x Number of stocks By adding the daily inventory cost obtained by the above formula over the relevant month, the total inventory cost for the relevant month Is obtained.

On the other hand, the sales opportunity loss cost due to out of stock is calculated using the following equation, assuming 10% of the out of stock quantity.

[0064]

[Equation 4] Daily lost opportunity cost = Selling price per item of the relevant product number x Out-of-stock amount By adding the daily lost opportunity cost calculated by the above formula for the days when the out-of-stock occurred, the corresponding month The total cost of lost sales is obtained.

The cost conversion unit 6 further adds the total inventory cost and the total sales opportunity loss cost obtained in this way to obtain the final evaluation value of the corresponding product number.

Further, the cost conversion unit 6 performs the above-described cost conversion processing for all product numbers, and sets the total sum as the final evaluation value of the process plan. That is, the evaluation value of the process plan is converted into the amount of money.

Since the evaluation value thus obtained is displayed on the display unit 7, when the operator who sees this evaluation determines that the process plan is insufficient and correction is necessary, the data input unit 2 is operated to correct the production plan data (process plan data) created by the process plan creation unit 1. When there is no need for correction, the process plan data is evaluated as optimal.

[0068]

EFFECT OF THE INVENTION The process plan evaluation system of the present invention graphically displays the process plan evaluation, which was conventionally performed manually, in terms of the stock amount and the out-of-stock rate, and uses these values converted into costs as evaluation values. Since it is configured to do so, there is an effect that the created process plan can always be quantitatively evaluated regardless of the experience of the side evaluating the process plan.

[Brief description of drawings]

FIG. 1 is a block diagram showing an electrical configuration of a process plan evaluation system of the present invention.

FIG. 2 is a diagram showing an example of a basic order receiving pattern.

FIG. 3 is a diagram showing an example of an ordering pattern for each product number.

FIG. 4 is a diagram showing an example of a product number-specific process plan created by a process plan creation unit.

FIG. 5 is a diagram illustrating an example of product number-specific inventory data created by a process plan creation unit.

FIG. 6 is a diagram showing an example of order forecast data for an arbitrary month.

FIG. 7 is a diagram showing an example of daily order forecast data by product number calculated by a daily order / part number order forecasting unit.

FIG. 8 is a graph showing transition data of stock and out-of-stock rate in the simulation unit.

FIG. 9 is a block diagram showing a more specific electrical configuration of a process plan creation unit.

FIG. 10 is a diagram showing order forecast data of this year based on the order receipt of the previous year.

FIG. 11 is a diagram showing order result data up to September of this year.

FIG. 12 is a diagram showing new order forecast data for October and thereafter.

FIG. 13 is a diagram showing production plan data for this year based on production performance data for the previous year.

FIG. 14 is a diagram showing production performance data up to September of this year.

FIG. 15 is a diagram showing new production planning data for October and thereafter.

FIG. 16 is a diagram showing inventory forecast data for October of this year onward.

FIG. 17 is a graph showing changes in inventory.

[Explanation of symbols]

 1 Process planning part 2 Data input part 3 Daily / part number order forecast part 4 Pattern storage part 5 Simulation part 6 Cost conversion part 7 Display part

Claims (1)

[Claims] [Claim 1] Production plan data for the current year based on the production results for the previous year and order forecast data for the current year based on the order results for the previous year are created, and the production plan data and the order forecast data are used for the current year. In addition to preparing a process plan creation unit that creates inventory forecast data, an input unit that corrects each data created in the process plan creation unit by an operator's input operation, and multiple monthly order forecast patterns. , A pattern storage unit for allocating and storing each of these order forecast patterns to each month within a predetermined period for each product number, and an order forecast amount for any month of an arbitrary product number created by the process plan creation unit, the pattern An order forecasting section for each day / part number that is assigned by day according to the order forecast pattern assigned to the relevant month of the relevant part number stored in the storage section , The stock quantity and out-of-stock rate are calculated from the order forecast data for the relevant month of the relevant product number assigned by this daily / product number order forecasting unit and the production plan data for the relevant month of the relevant product number created by the process plan creation unit. A simulation unit that performs a simulation, a cost conversion unit that converts the inventory amount and the out-of-stock rate obtained by this simulation unit into costs according to a predetermined calculation formula, and a display unit that displays the cost obtained by this cost conversion unit as an evaluation value. And a process plan evaluation system characterized by including.