JP2002109190A - Method and device for optimizing new product manufacture - Google Patents

Abstract

PROBLEM TO BE SOLVED: To decide the main production plant, the production quantity of a new product and the equipment investment level, thereby minimizing a cost increase generated by introducing new production. SOLUTION: A loss amount increase calculating part 18 calculates a loss amount increase on the basis of predicted demand of the product in each period of the new product and the modified predicted demand of the product. When the number of products distribution processing part 6 distributes the modified quantity of the products by each period and by each production plant and decides a distribution pattern, respective cost increase calculating parts 20, 22 and 24 respectively calculate variable cost increase, fixed cost increase and a risk in terms of cost increase. A cost evaluation processing part 10 calculates the total sum of the respective cost increases about the modifications and the distribution pattern. The total sums of cost increases are also calculated in the other distribution patterns and modified patterns. A production plant, the production quantity and an equipment investment level specified by the distribution pattern of the minimum total sum among the total sums are adopted.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and an apparatus for optimizing production of a new product, and more particularly to a production base for producing a new product when drafting a production plan when a plurality of products are produced at a plurality of production bases. And an optimization method for effectively determining the number of production units from the viewpoint of cost.

[0002]

2. Description of the Related Art When a new product is put on the market at a plurality of production sites (also referred to as production locations) such as an automobile manufacturer, the demand of the new product is predicted. It is necessary to distribute the predicted demand (production plan) to existing production sites. In this case, it is desired to formulate a production plan that enables efficient production from a cost perspective by minimizing the increase in cost associated with the introduction of a new product.

For example, Japanese Patent Application Laid-Open No. Hei 10-207955 discloses a cost management method and system capable of minimizing costs required for producing products at a plurality of production sites.

[0004]

However, in the conventional example, the production base for producing the product has already been determined, and then the distribution and planning of the production amount are performed. In other words, in the conventional example, costs such as capital investment and labor costs required for the production of a new product are not necessarily the same at all production bases. It does not take into account how much capital investment and production at the production base is optimal from a cost perspective.

SUMMARY OF THE INVENTION The present invention has been made to solve the above problems, and an object of the present invention is to provide a production base, production quantity, and capital investment for a new product which minimizes the cost increase caused by the introduction of a new product. An object of the present invention is to provide a new product production optimizing method and a new product production optimizing device for determining a level.

[0006]

In order to achieve the above object, a method for optimizing production of a new product according to the present invention provides a method for optimizing a demand for a new product according to a distribution pattern designated by an input. Calculate the sum of loss cost increase, variable cost increase, fixed cost increase and risk conversion cost increase that can occur at each production site by producing a new product when allocated to production sites, and the total is minimized. By determining the distribution pattern, a production base for producing a new product, a production amount at each production base, and a capital investment level for each production base are determined.

The new product production optimizing apparatus according to the present invention generates a new product at each production site by producing a new product when the predicted amount of the new product is distributed to each production site in accordance with the input and designated distribution pattern. Cost increment calculating means for calculating possible loss cost increments, variable cost increments, fixed cost increments, and risk conversion cost increments, and the sum of the respective cost increments calculated by the cost increment calculating means in accordance with an input designated distribution pattern. And a cost evaluation processing means for determining the production pattern of the new product, the amount of production at each production facility and the level of capital investment at each production facility by specifying the distribution pattern that minimizes the sum of the cost increments. Features.

[0008]

Preferred embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing an embodiment of a new product production optimizing apparatus according to the present invention. The new product production optimizing device according to the present embodiment includes a demand forecast amount input processing unit 2, a demand forecast amount rearranging unit 4, a production unit distribution processing unit 6, a cost increment calculation processing unit 8, a cost evaluation processing unit 10.
And an optimum production number output unit 12. The demand forecast input processing unit 2 receives the demand forecast numbers of the input new products in each period and stores them in the demand forecast storage unit 14. The demand forecasting amount rearranging unit 4 rearranges the input predicted demand number of new products, and temporarily stores the result in the data temporary storage unit 16. Production number distribution processing unit 6
Allocates the number of new products after rearrangement to each production site and each period, and temporarily stores the result in the data temporary storage unit 16. The cost increment calculation processing unit 8 calculates a loss cost increment,
It has calculation units 18, 20, 22 and 24 for calculating the variable cost increment, the fixed cost increment and the risk conversion cost increment, respectively. The cost evaluation processing unit 10 calculates the total sum U of the cost increments based on various rearrangement patterns of production units and distribution patterns of production units. The optimal production number output unit 12
From the data calculated by the cost evaluation processing unit 10, data in which the total sum U of the cost increment is the minimum among the combinations of the production number M of the new product M and the investment level P is presented together with the cost details.

Further, in this embodiment, a master file 26 is provided in advance, and this file includes tables 28, 30, and 32 for threshold production volume, variable cost coefficient and fixed cost coefficient, A cost function used for calculating the cost is registered in advance. FIG. 2 shows an example of the data configuration of the threshold production number table 28 set for each capital investment level, FIG. 3 shows an example of the data configuration of the variable cost coefficient table 30, and FIG. An example of the data configuration is shown.

A feature of this embodiment is that, when a plurality of products are produced at a plurality of production sites, the cost of a new product and the amount of capital investment at which production site is determined. This is to make it possible to formulate a production plan from a comprehensive point of view. As a result, the costs incurred can be minimized.

Here, when a new product M is newly introduced into the production bases 1 to N, which are a plurality of production factories for producing the existing products 1 to M-1, an increase in cost is minimized. The processing will be described as an example.

By the way, costs that are considered to increase when a new product having a predicted demand amount is produced can be categorized into loss costs, variable costs, fixed costs, and risk conversion costs. The loss cost is a cost of opportunity loss or inventory loss that is expected to be caused by producing a new product ahead of or behind demand, and is a cost incurred by the seller. Opportunity loss is a loss caused by a lack of supply due to a shortage of supply despite demand. Inventory loss is a loss caused by holding inventory due to shortage of demand despite supply. The variable cost is a cost incurred on the producer side, for example, a cost such as an overtime cost incurred due to production exceeding the production plan. The fixed cost is a cost incurred on the producer side, and is a fixed cost required for production as planned. For example, there are personnel costs and equipment fixed costs when there is no overtime. The risk conversion cost is a cost equivalent to the cost conversion of the risk due to the multiplexing of the production bases of the new product M, and is a cost incurred by the seller and the producer as a whole. In the present embodiment, the multiplexed risk cost is treated as a function of the multiplicity in consideration of the risk conversion cost. “Multiplicity” means that any one product is produced in parallel at a plurality of production sites. If the multiplicity is large, the complexity of production planning increases, which is troublesome, and therefore the cost required for planning increases. In addition, although capital investment is increased, it is possible to flexibly cope with an increase in production volume. On the other hand, if the multiplicity is small, if the actual demand exceeds the forecasted demand, it will be difficult to respond to the increase in the production volume, and risk will occur in the sense that the level of opportunity loss and inventory loss will increase. . Since there is a relationship that the advantage when the multiplicity is large and the advantage when the multiplicity is small, it is necessary to determine the optimal multiplicity in consideration of various factors.

It is considered that minimizing the above-mentioned increase in cost is an optimum production plan and capital investment when introducing a new product. That is, U = ΔD + ΔH + ΔK + ΔL (1) The purpose is to find the production number x and investment level P of a new product that minimizes the total sum U of the cost increments that can be expressed by the evaluation formula:
Where ΔD is the loss cost increment, ΔH is the variable cost increment, Δ
K is a fixed cost increment, and ΔL is a risk conversion cost increment.
As will be apparent from the description of the processing described later, the production quantity x and the investment level P are variables when obtaining ΔD, ΔH, ΔK, or ΔL.

As is apparent from the above equation (1), each of the four cost increment variables is a cost increment value. Therefore, to minimize the value obtained by summing the cost increment values,
That is, each of the increment values ΔD, ΔH, ΔK, and ΔL is minimized. In the present embodiment, the purpose is to find each cost increment that minimizes U, that is, the number of production units x and the investment level P. First, production volume x and investment level P
Before obtaining, each variable used in the present embodiment is defined as follows.

Production sites: 1, 2,..., N..., N Each production site can be regarded as a production factory.

Product type: 1, 2,..., M,... M Here, 1 to M−1 are assumed to be existing products, and M is a new product, and the number of products is determined.

Production planning period: 1, 2,..., Q,... Q Investment level: 1, 2,. Here, investment level 1
Represents a level at which a product M can be produced at the production site n without changing the production capacity. Investment level 2-P
Is a level at which the number of production units is increased while making it possible to produce the product M, and an increase ratio of the number of production units is set in advance for each. The larger the value of the level value p, the higher the increase ratio is set.

X _nqM [vehicles / period]: Production base n of product M, number of units produced in period q Corresponds to the number x of production units described above.

X ¹ _pnq [vehicle / period]: First threshold value at production base n for period q, investment level p X ² _pnq [vehicle / period]: Period q for production base n, investment X ³ _pnq [vehicles / period]: period q at production base n, third threshold at investment level p The threshold production quantity X fluctuates The ratio indicates the number of production units in which the ratio of the cost to the number of units (hereinafter referred to as “variable cost coefficient”) changes. Here, three threshold values are prepared as a specific example. The range of regular operation without overtime is X ¹
_pnq, overtime there is an upper limit number X ² _pnq of the holiday without going to work, the upper limit of the number of overtime on holidays there and so X ³ _pnq, respectively, in association with the actual production activities to set the threshold. In addition, if there is overtime or work on holidays, an increase in labor costs is expected, and the variable cost coefficient is changed accordingly. In the present embodiment, three types of thresholds are set, but the number of thresholds may be set according to the task to which the present invention is applied.

A ¹ _n [yen / vehicle]: Variable cost coefficient when the production volume is less than X ¹ _{pnq at} production base n a ² _n [yen / vehicle]: the production volume at production base n is X ¹
Variable cost coefficient when the _value is larger than _{pnq and} within X ² _pnq・ a ³ _n [yen / unit]: The production number at production site n is X ²
variations in the case within larger X ³ _PNQ than _PNQ cost factor · [Delta] D [yen]: Loss cost increment · [Delta] H _nq [Yen / Period: variable cost increment. It is obtained from the sum of the variable costs generated in the production base n and the period q in the new product M.

ΔK _np [yen]: fixed cost increment. New product M
Is determined by the sum of the fixed costs incurred when the investment level p is set at the production base n.

ΔL [yen / period]: Risk conversion cost ・ Y _qM [vehicle / period]: predicted number of new products M in period q

(Equation 1) Next, a process according to the present embodiment will be described with reference to a flowchart shown in FIG.

In step 101, the demand forecast input unit 2 receives the demand forecast numbers Y _{1M to} Y _QM of the new product M in each of the periods 1 to Q, and receives the demand forecast quantity storage unit 14. To store.

In step 102, the demand forecasting quantity rearranging section 4 executes the input demand forecasting machine number Y _1M-
By rearranging Y _QM forward or rearward, rearrangement is performed to smooth out the predicted number of units. This rearrangement is designated by input from an input device (not shown). The constraints when sorting are:

(Equation 2) And The sorted demand prediction amounts are temporarily stored in the data temporary storage unit 16.

In step 103, the loss cost increment calculator 18 calculates the loss cost increment by the following equation (3).

[0027]

(Equation 3) In the above equation, the function f is a function expressing the cost of opportunity loss that occurs in accordance with the difference between the supply amount and the supply amount after the rearrangement (demand amount−supply amount) in the corresponding period. The function g is a function expressing an inventory loss that occurs in accordance with the difference (supply amount−demand amount). It is assumed that the functions f and g are registered in the master file 26 in advance. The sum of ΔD calculated by this equation is stored in the temporary data storage unit 16.
Is temporarily stored.

In step 104, the production number distribution processing unit 6 distributes the rearranged number of the new products M to each of the production bases 1 to N and each of the periods 1 to Q. This distribution pattern is input and specified from an input device (not shown). The production volume of each product other than the new product M per production base and period is as follows:
Assume that you have already been given. The constraints when distributing are:

(Equation 4) Here, X ^max _pnq indicates a production base n when the investment level P is the maximum, and a maximum threshold production number in the period q.
^It has the largest value among ¹ _pnq , X ² _{pnq and} X ³ _pnq . This means the number of vehicles that can be produced to the maximum extent possible for investment. The threshold production number X is a value obtained from the threshold production number table 28 of the master file 26. The first term of the above equation (5) is the production number of the new product M, and the second term represents the sum of the production numbers of the existing products 1 to M-1. The distributed production number is temporarily stored in the data temporary storage unit 16 as a distribution pattern.

In step 105, the variable cost increment calculation unit 20 calculates the variable cost increment using the number of vehicles once determined in step 104 as a given value.
At that time, φ is first defined. φ is the following equation (6)
It can be expressed by (8).

[0030]

(Equation 5)Each fixed value φ¹ _pnq, Φ^Two _pnq, Φ^Three _pnqIs the threshold production unit
Number X¹ _pnq, X^Two _pnq, X ^Three _pnqTo existing products other than new product M
This is a fixed value obtained by subtracting the total number of vehicles produced. Sand
That is, the value represented by each fixed value is
Is considered to be the capacity at each production site when
And production of new product M with the number within this fixed value range
Becomes possible. Note that the production volume of each threshold is
Pre-registered in the threshold production number table 28 of the file 26
It is assumed that

Incidentally, it is necessary to determine the investment level P for each production base n as a premise for obtaining the increment of the variable cost. FIG. 6 shows the flow. In FIG. 6, the number of new products M and other products 1
In other words, an investment level is determined such that the total number of M-1 production units falls within the range of the maximum number of production units.

In step 124 of FIG.
If it is determined that φ ³ _pnq is smaller than x _nqM in the period q, that is, there is no _room to produce the new product M, the investment level is increased (step 125). When it is determined that there is spare capacity, it is similarly checked whether there is spare capacity for the next period (steps 126 and 124). Then, when it is determined that there is room in all the periods (step 127),
The investment level P ^{* at the} production site n is determined (step 128). This process is performed for all production bases (steps 129 and 130).

As described above, when the investment level for each production site is determined, the variable cost increment is calculated by the following procedure.

When x _nqM ≦ φ ¹ _pnq , ΔH _nq = a ¹ _n × x _nqM (9) and phi ¹ _PNQ <When ^{_{x nqM ≦ φ 2 pnq ΔH nq}} = a 1 n × φ 1 pnq + a 2 n × (x nqM -φ 1 pnq) ······ (10). At the other ^{_{φ 2 pnq <x nqM ≦ φ}} 3 pnq, ΔH nq = a 1 n × φ 1 pnq + a 2 n × (φ 2 pnq -φ 1 pnq) + a 3 n × (x nqM -φ 2 pnq ) (11)

Here, a ¹ , a ² and a ³ are variable cost coefficients [yen / vehicle] for each threshold production number for each production base,
It is registered in advance in the variable cost table 30 of the master file 26 in consideration of personnel costs and the like. As described above, the total of the variable cost increments (formulas (9) to (11) above) is totaled for the production base for the period

(Equation 6) Becomes

In step 106, the fixed cost increment calculation unit 22 extracts the fixed cost increment for each production base at the investment level P obtained in step 105 from the fixed cost coefficient table 32 of the master file 26 and totals it. That is,

(Equation 7) In step 107, the risk conversion cost increment calculation unit 2
4 calculates the increment of the risk conversion cost as follows. First, the number of production bases of the new product M in the period q is multiplicity T _q
Is defined as Let the cost increment of the multiplicity T _{q be} ΔL _q = h (T _q ) (14). The function h is a cost function registered in advance in the master file 26, but is a downwardly convex function as shown in FIG. 7, and the lowest point is the optimum multiplicity. As a result of this processing, the sum of the risk conversion cost increments is

(Equation 8) Becomes

In step 108, the cost evaluation processing section 10 calculates each cost increment calculated as a result of the above processing, more specifically, the equations (3), (12), (13), and (1).
The total sum U of the respective cost increments obtained by 5) is calculated by the above equation (1). U and new product M
The production number x _{nqM and} the investment level P for each production base are stored in the data temporary storage unit 16 in association with the distribution pattern as a processing result in the distribution pattern input and designated in step 104.

After performing the above calculations, the sum of the cost increments U
If it is desired to find another production distribution pattern in order to obtain the production number x _nqM of the new product M and the investment level P for each production base, the steps 104 to 108 are repeated. If you are asked to sum U about the distribution pattern of production volume that you want to try, this time investment of each production number _x nqM as well as the production base of the new products M the sum U becomes smaller cost increment in the rearrangement pattern of other production volume Steps 102 to 109 are repeatedly executed to obtain the level P. Each time the above processing is repeatedly executed, the total sum U, the number x _nqM of manufactured new products M, and the investment level P for each production base are stored in the data temporary storage unit 16.

As described above, the total sum U is calculated based on the rearrangement pattern of the various production units and the distribution pattern of the production units.
And the production level x _nqM of the new product M and the investment level P for each production site are calculated.
2 outputs the data with the minimum total sum U of the cost increments in the combination of the production number M of the new product M and the investment level P together with the details of the cost.

As described above, according to the present embodiment, it is possible to determine the production base, production volume, and investment level with the minimum cost. In addition, the present invention, when starting the production of a new product, based on the assumption that an existing production site that is producing an existing product is used, and determined a production site or the like that produces a new product.
It can be applied to the case where a new production base is provided, and can also be used for redistribution of existing products.

[0041]

According to the present invention, a production base for producing a new product of a predicted demand amount, a production amount and a capital investment level at each production base are calculated by adding a loss cost increment, a variable cost increment, a fixed cost increment, and a risk conversion cost. It can be determined that the sum of the increments is minimized.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an embodiment of a new product production optimizing apparatus according to the present invention.

FIG. 2 is a diagram illustrating a data configuration example of a threshold production number table according to the present embodiment.

FIG. 3 is a diagram illustrating a data configuration example of a variable cost coefficient table according to the present embodiment.

FIG. 4 is a diagram illustrating a data configuration example of a fixed cost coefficient table according to the present embodiment.

FIG. 5 is a flowchart showing a flow of processing for obtaining an optimum production number of new products and the like in the present embodiment.

FIG. 6 is a flowchart showing a flow of processing for determining an investment level for each production base in the present embodiment.

FIG. 7 is a diagram showing a relationship between multiplicity and an increase in risk conversion cost in the present embodiment.

[Explanation of symbols]

2 Demand forecast amount input processing unit, 4 Demand forecast amount rearranging unit, 6 Production unit distribution processing unit, 8 Cost increase calculation processing unit, 10 Cost evaluation processing unit, 12 Optimal production unit output unit, 14 Demand forecast amount Storage unit, 16 data temporary storage unit, 18 loss cost increment calculation unit, 20 variable cost increment calculation unit, 22 fixed cost increment calculation unit, 24 risk conversion cost increment calculation unit, 26 parameter file, 28 threshold production table, 30 Variable cost coefficient table, 3
2 Fixed cost coefficient table.

Claims (2)

[Claims] When a demand forecast amount of a new product is distributed to each production site of an existing product according to an input and specified distribution pattern, a loss cost increment and fluctuation that can occur at each production site by producing a new product. Calculate the sum of the cost increment, fixed cost increment, and risk conversion cost increment, and identify the distribution pattern with the smallest sum to the production base that produces the new product, the production volume at each production base, and each production base A method for optimizing production of new products, characterized by determining the level of capital investment. 2. When a demand forecast amount of a new product is distributed to each production site according to an input-specified distribution pattern, an increase in loss cost, an increase in variable cost, and an increase in variable cost that can occur at each production site by producing a new product. A cost increment calculating means for calculating a fixed cost increment and a risk conversion cost increment; and a sum of the respective cost increments calculated by the cost increment calculating means in accordance with the input-specified distribution pattern, and the sum of the cost increments is minimized. A new product production optimizing device comprising: a new product production base by specifying an allocation pattern; a cost evaluation processing means for determining a production amount at each production base and a capital investment level at each production base. .