WO1997009686A1

WO1997009686A1 - System for assisting planning of component stock schedule

Info

Publication number: WO1997009686A1
Application number: PCT/JP1996/002563
Authority: WO
Inventors: Shino Takahashi; Hideaki Matoba; Mitsuhiro Enomoto
Original assignee: Hitachi, Ltd.
Priority date: 1995-09-08
Filing date: 1996-09-09
Publication date: 1997-03-13
Also published as: JPH0973491A

Abstract

To determine the stock quantities of components of products whose demand change is great and products whose demand prediction is difficult, to quantitatively evaluate the ability to meet customers' requirements and the stock risk, to establish a component stock plan on the basis of a clear index, or to determine the stock quantities of components in accordance with mutual compatibility of components of various models of products and to establish a component stock plan of components while taking the demand changes of the models into consideration, a system for assisting the planning of component stock comprises a step of inputting information about prediction of demand for each model, used components/used member constitution information about each model, stock cost information of components and product sale profit information for calculating the sale profit of each product, a step of assuming a maximum production quantity of each model, a step of setting a component stock quantity in accordance with the maximum production quantity so assumed, a step of calculating an expected value of the stock cost at the stock quantity so set and an expected value of the product sale profit at the production quantity so assumed, and a step of conducting cost trade-off evaluation of both expected values.

Description

Description Parts inventory planning support system Technical field

INDUSTRIAL APPLICABILITY The present invention can be widely used in the manufacturing industry to procure necessary parts or members in anticipation of product demand, especially for products where the life of one model is short and the parts or members to be procured are constantly changing. And a suitable parts inventory planning support system. Background art

Generally, a production plan is drafted in advance, taking into account the time required for parts procurement and personnel arrangements. Parts will be arranged based on this plan, and products will be produced at the corresponding production time. In this case, the quantity of parts to be supplied is the amount obtained by adding the production rate of the product plus the additional alpha, such as the yield.

In addition, as a method of planning a part plan regardless of the product production plan, there is a method of arranging parts based on the consumption speed of individual parts.

This is particularly used in a parts-centric production method (a method in which parts are kept in stock as they are and products are produced according to customer requirements).

In the procurement of parts with the added amount of product production plus the additional amount of yield, etc., if the demand forecast of the product deviates, it is necessary to procure parts not enough to meet actual demand Product cannot be produced. In this way, the accuracy of demand forecasts determines the ability to respond to customers. This is an effective part procurement method for products that can predict demand with high accuracy, but there is a great risk of reducing the degree of response to customers for products with large fluctuations in demand and products that are difficult to predict. is there. Looking at the procurement status of parts in more detail, it is often the case that even if there are parts and materials commonly used between models, product production can not be done because there are no dedicated parts and parts for each model .

This is the case where the demand forecast for the entire model (model group) is almost the same as the power demand ^ it is difficult to forecast the demand for individual models. Also, in the method of arranging parts based on the consumption speed of individual parts, in the case of products with a short life cycle of each product model, the model configuration produced at the same time always changes, and the parts used accordingly Because of the change, when the demand for parts is judged based on the past consumption speed of parts, there is a problem that defective inventory and missing parts are generated.

As a method of drafting a parts procurement plan in consideration of fluctuations in demand, there is a “sequentially determined production planning system” described in Japanese Patent Application Laid-Open No. Hei 4-599796, and a production plan is created in accordance with the accuracy of demand forecasting. It introduces a method in which materials are arranged in multiple stages, and materials are arranged in multiple stages in the order of common unit parts → model group common parts—dedicated parts.

Even with this method, if the procurement lead time for dedicated parts is long, the accuracy of the initial demand forecast determines the degree of response to customers.

Also, there is no mention of how much margin should be given to material forecasts for demand forecasts.

The purpose of the present invention is to quantitatively evaluate the ability to respond to customers and inventory risk, targeting parts inventory settings for products with large fluctuations in demand and for products for which demand is difficult to predict. The purpose is to provide a system for making inventory plans.

Another object of the present invention is to provide a system for setting a stock amount of parts according to the degree of sharing of parts between models and planning a parts inventory plan in consideration of fluctuations in demand between models. Disclosure of the invention

The objectives are: demand forecast information for each model, demand forecast information for each model group, used parts and used component configuration information for each model, inventory cost information for calculating the cost of having parts in stock, Means for entering product sales profit information for calculating product sales profit for each product, and product sales rules that define product sales standards, as well as production volume for the entire model group and maximum production volume for individual models A production quantity provisional setting function, a parts inventory quantity setting function to set parts inventory quantity, an inventory cost calculation function to calculate expected value of inventory cost for the set inventory quantity from demand forecast, and an assumed production quantity Sales profit calculation function that calculates the expected value of product sales profits from demand forecasts, and a cost display that displays the expected values of inventory costs and product sales profits for each production volume. It can be achieved by parts inventory planning system and a trade-off display function.

In addition to the above, the other objectives are to determine the production volume of the entire model group set by the provisional production volume setting function, the maximum production volume of individual models, and the inventory of parts according to the degree of sharing between the components. This can be achieved by the part inventory setting function.

Demand forecast information for each model, demand forecast information for each model group, used parts and used component information for each model, inventory cost information for calculating the cost of having parts in stock, and products for each product Assuming the means for inputting product sales profit information for calculating sales profit and product sales rules that define product sales standards, the production volume of the entire model group, and the maximum production volume of individual models Temporary production volume setting function, component inventory volume setting function to set parts inventory volume, inventory cost calculation function to calculate expected inventory cost at set inventory volume from demand forecast, product sales at assumed production volume Parts inventory planning that has a product sales profit calculation that calculates expected profits from demand forecasts and a cost trade-off display function that displays the expected values of inventory costs and product sales profits for each production volume The system enables quantitative evaluation of the degree of customer responsiveness and the risk of inventory cost taking into account demand fluctuations. This makes it possible to formulate a parts inventory plan that takes into account the ability to respond to customers.

In addition, the parts inventory setting function that sets the parts production quantity according to the production quantity of the entire model group set by the production quantity provisional setting function, the maximum production quantity of individual models, and the degree of commonality of each part between models According to this, the inventory of parts can be set according to the degree of sharing of parts between models, and a parts inventory plan that takes into account fluctuations in demand between models can be made. BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a functional configuration of a parts inventory planning support system according to the present invention, FIG. 2 is a demand forecast information format, and FIG. 3 is a used component / member configuration information format. Fig. 4 shows inventory cost information, product sales profit information, and product sales rule format.Fig. 5 shows an example of demand distribution, and Fig. 6 shows demand distribution. Fig. 7 is a diagram showing an example of parts analysis, Fig. 8 is a diagram showing a method of setting stock amount, Fig. 9 is a diagram showing an example of using a demand balance coefficient, Fig. 10 is a diagram showing the inventory cost calculation procedure. Fig. 11 is a diagram showing the relationship between the demand situation / set stock amount / sellable unit. Fig. 12 is a diagram showing the demand cycle using the life cycle. Fig. 13 shows an example of the amount assumed. Fig. 13 shows the cost trade-off evaluation. FIG. 14 is a diagram showing a screen for presenting a producible amount, FIG. 15 is a diagram showing an example of automatic setting of a demand forecast value, and FIG. A parts inventory planning support system according to the present invention FIG. 3 is a diagram showing a specific example of a device configuration.

BEST MODE FOR CARRYING OUT THE INVENTION

Hereinafter, an embodiment of a parts inventory planning support system according to the present invention will be described in detail.

FIG. 1 shows the overall functional configuration of the system including the contents described in the claims.

In Fig. 1, 111 is a function for provisional production volume setting, 112 is a function for setting parts inventory, 1-3 is an inventory cost calculation function, 114 is a product sales profit calculation function, and 115 is a cost. The trade-off evaluation function, 116 is an interface function, and 117 is a block that implements a demand forecast value setting function.

The data format of the information and rules described in Fig. 1 are shown in Figs. 2 to 4.

Hereinafter, the processing content of each function in FIG. 1 will be described in detail.

[1-1 Production volume temporary setting function]

This function satisfies the customer responsiveness assuming the customer responsiveness of the model group (that is, what percentage of the expected fluctuations in demand is covered) and the customer stress of each individual model. <For example, assuming that the demand distribution of model A occurs between 1000 and 2000 with the probability shown in Fig. 5, In order to satisfy 0%, the product production volume is set at 170,000 units.

Here, 90% of customer responsiveness means that all customers' requests are met with a 90% probability.

Fig. 6 shows the demand distribution of models A to E and the demand distribution of model group 1. If all customer responsiveness is set to 90%, the production volume of each product and the product as a model group The production volume is set as shown in Fig. 6. You.

Assuming the production volume of these products, the following operations 1-2 to 1-4 are performed. In 1-5, various customer service levels are assumed, and between 1-2 and 1-4 The cost trade-off evaluation is performed from the result of the calculation.

Note that a model group refers to a group of models that share a common user layer based on product functions and performance, price range, target users, and so on. Although the market size of the model group as a whole is easy to grasp from the manufacturer side, the choice between models depends largely on the user's preference, and it is difficult to grasp the demand.

[1-2 Parts inventory setting function]

In this function, the parts inventory is set based on the product production of individual models and the product production of the model group set in Function 1-1.

(1-2-1. Parts analysis)

Parts used for each model · Parts are classified according to the degree of sharing between models based on the used component configuration information. Fig. 7 shows an example of classification.

(1 — 2— 2. Parts inventory setting)

The following are four examples of inventory setting methods. Fig. 8 shows the conceptual diagram.

1) is the parts inventory = ∑ (Product production of individual models using the part

X parts)

2) is the inventory of parts = min {∑ (Product production of individual models that use the parts X number of parts), f (Product production of model group)}

3) is the inventory of parts = min {製品 (Product production of individual models using the parts X number of parts Z units), ί (Product of model group products) Production), f (∑ (Production volume of model group products-minimum product production volume of individual models that do not use the part))}

4) is the parts inventory = min n f (demand balance coefficient, demand distribution of individual models that use the part)), f (model group product production),: f (∑ (model group product production Minimum product output of individual models that do not use the parts))))

The functions in the above 1) to 4) mean the functions for deriving the component quantity from the product production shown in the parentheses, and are, for example, as follows.

f (model group product production) = model group product production x m a X

(Z number of parts in individual model using the part)

f (∑ (model group product production volume-minimum product production volume of individual models that do not use the part))

= (Production volume of model group-Minimum product volume of individual models that do not use the part) x max (Z units of the individual model that uses the part)

Also, f (demand balance coefficient, demand distribution of individual models using the part) indicates the following.

First, the demand balance coefficient is a value that indicates the correlation of demand among multiple models. For example, if it is expected that there is a correlation between the demands of model A and model B such that model A sells and model B does not sell, then the demand balance coefficient between model A and model B will be 1. The quantity of parts shared by model A and model B is obtained by superimposing the demand distribution of A and B and calculating the quantity of parts to obtain the required degree of customer service. Conversely, when there is no correlation between model A and model B, the demand balance coefficient is 0, and the parts shared by model A and model B are set to the component quantities calculated from the sum of the product production volumes of the individual models. .

Parts used by three or more models are divided into combinations with a demand balance coefficient of 1, and in those combinations, the component quantities are calculated from the superposition of the demand distribution, and the sum of those component quantities is calculated. This is regarded as the total amount of parts for the model that uses the parts (Figure 9 shows a conceptual diagram).

[1-3 Inventory cost calculation function]

Function Calculates the inventory cost when the parts inventory set in 1-2 is held.

Here, the inventory cost is

1) Costs incurred by not producing at the expected time but owning parts (eg, storage costs, etc.)

2) Interest that would have been gained by depositing if the part was not purchased until the required time

3) The total cost of the dead stock (ie, the cost of parts) when the parts are finally discarded because they are not used.

One example of the inventory cost calculation method will be described with reference to FIGS. 10 to 12. FIG. 10 shows a processing flow of inventory cost calculation.

(1 0-1. Calculation of remaining parts)

Here, the amount of parts that are not consumed in the assumed period of the set inventory and remain in inventory after that is calculated.

Fig. 11 shows the relationship between the demand and the number of units that can be sold for the set inventory amount. In the demand situation 1 in Fig. 11, demand for model A is 100 units, demand for model B is 200 units, 300 units are available for sale, and 100 parts are sold. The number of parts is 0, the number of parts C is 0, and the remaining parts amount. The demand in Figure 11 Situation 2 shows a case where the demand for model A is 200 units and the demand for model B is 200 units, and there is a shortage of parts that are common parts. In this case, which one is given priority for sale is determined from the product sales rules. An example of product sales rule data is shown in Fig. 4-1C. This is the priority assigned to each model. The remaining parts are calculated assuming that the one with the higher priority is sold first. For example, a model whose sale is recommended has a higher priority. In the case of Fig. 11, it is assumed that model A, which has the highest priority, will be sold. .

(1 0-2. Stock period Z Dead stock calculation)

Calculate how long the parts in stock at 10-1 have been in stock afterwards, and how many parts will be in stock at the end of product life. As this method, for example, there is a method using a life cycle curve (FIG. 12) of each model. This assumes the demand in the next cycle (June in the example of Fig. 12) from the position on the life cycle in the target period of the inventory plan (May in the example of Fig. 12). If the standard demand is 100,000 units, the next month is assumed to be 800 units. In this way, we estimate how much quantity will remain in inventory until the end of the life cycle, and finally how many parts will remain (dead stock).

(10-3. Inventory cost calculation based on assumed demand)

The inventory cost is calculated from the amount in stock each month. For example, it is calculated by the following Equation 1.

C = ∑∑ {S jkX (Hk + LkX I) + D jkx Lk}… [Equation 1] j k

C: Inventory cost S jk: Stock quantity of part k when product production volume was j x stock months Djk: Dead stock volume of component k when product production volume was j

Hk: Storage cost for parts k

Lk: Parts k parts cost

I: Interest rate

j: Serial number assigned to the set of product production of each model

For example, the product production set of (model A 100 units, model B 100 units, ...) is set to j = 1.

(1 0-. Inventory cost expected value calculation)

10. Multiply the “inventory cost at the assumed demand” calculated in step 3 by the probability of occurrence of the demand to calculate the expected value of the inventory cost at the assumed product production volume.

For example, it can be calculated by the following equation (2).

C P = ∑C i X P i (Equation 2)

1 where

C P: Expected value of inventory cost

C i: Inventory cost of the i-th demand group

Pi: Probability of occurrence of the i-th demand group

i: Serial number assigned to the set of demand for each model

For example, a set of demand amounts of (model A100, model B100,...) Is set to i = 1.

[1-4 Product Sales Profit Expected Value Calculation Function] Calculates the number of units that can be sold from product production and demand, and calculates the expected value of the resulting profit. Equation 3 shows an example of a product sales profit calculation formula.

P R = ∑∑∑ {SSi j 1 X U 1 X P i} (Equation 3 i j 1 where

P R: Expected sales profit

SSijl: The number of units that can be sold for product 1 when i is the demand and j is the product production

U1: Unit 1 unit sales profit

Pi: Probability of occurrence of the i-th demand group

i: Serial number assigned to the set of demand for each model

For example, the set of demands for (model A 100 units, model B 100 units, ...) is i = 1

j: Serial number assigned to the set of product production of each model

For example, set the product production set of (model A100, model B100,...) To j = 1.

[1-5 Cost trade-off evaluation function] Extracts the plan with the minimum total risk from inventory costs and product sales profits obtained at various customer responsiveness levels.

Total risk = Product sales profit—Inventory cost

Can be considered.

In addition, instead of product sales profit, we use the disadvantage of loss of product sales machinery, so that total risk = loss of product sales machinery loss + inventory cost

It can also be considered.

Here, the product sales machine loss penalty is the expected value of the profit that was missed because the number of units that can be sold was smaller than the demand. it can.

L = ∑∑∑ {(Dil— SSijl) U 1 X P i} (Equation 4) i j 1 where

L: Expected loss of product sales opportunity loss

Demand for product 1 in case of Dil: i demand

U1: Unit sales profit for model 1

Pi: Probability of occurrence of the i-th demand group

i: Serial number assigned to the set of demand for each model

For example, the set of demand quantity of (model A 100 units, model B 100 units, ...) is i = 1

j: Serial number assigned to the set of product production of each model

For example, set the product production set of (model A 100 units, model B 100 units, ...) to j = 1.

[1-6 Interface Function] Figures 13 and 14 show examples of interface screens.

Figure 13 shows the cost trade-off evaluation result screen, with the horizontal axis representing product production and the vertical axis representing cost. In addition, the horizontal axis can be set as the customer responsiveness.

Fig. 14 shows the screen for presenting the producible quantity, showing the correlation between the maximum producible quantities between multiple models based on the inventory plan for each part.

Other interface functions include demand forecast information for each model, demand forecast information for model groups, used parts and used component configuration information for each model, and parts. Inventory cost information for calculating the cost incurred by having inventory. Product sales profit information for calculating product sales profit for each product, and product sales rules that define product sales standards. The cost trade-off evaluation is performed using the changed data.

In addition, when multiple processing methods are set in the temporary production volume setting function, parts inventory setting function, inventory cost calculation function, and product sales profit calculation function, there is a screen for specifying the processing method. Perform a cost trade-off evaluation using the method specified by.

[1-7 Demand forecast value setting function]

This function is to set the demand forecast value used in 111 to 116. Demand forecasts are rarely made in advance with a distribution, and in many cases only standard demand is forecast. For example, for model A, only one value is presented, such as 1500. In this case, this function generates a demand distribution with minimum, peak (standard demand) and maximum values based on these standard demand forecasts. Several types of probability distributions are provided inside the system. The user automatically generates the demand distribution by inputting the type of the probability distribution, the standard demand unit, the number of divisions, and the unit unit.

For example, in the example of Fig. 15, the demand distribution generated when the standard demand is 100 vehicles, the probability distribution type is normal distribution, the number of divisions is 10 and the number of units is 5 is shown.

Here, the number of sections indicates how many regions the probability distribution is divided into.

FIG. 16 shows an example of a specific device configuration of the parts inventory planning support system according to the present invention. 16-1 is a magnetic disk drive, and data such as individual model demand forecast information, model group demand forecast information, parts and material composition information, inventory cost information, product sales profit information, parts usage rules, product sales rules, etc. Data. 16-2 is a processing device, which performs various operations for executing the present invention. 16-3 is a floppy disk input device, 16-4 is a keyboard as an auxiliary input device, and 16-5 is a printer as an auxiliary output device.

In the parts inventory planning support system according to the present invention, risk assessment is performed by changing the customer responsiveness of each model in various ways, so that management decisions can be made on sales strategies such as setting models that focus on sales and determining discount rates. It can also be used for Industrial applicability

According to the parts inventory planning support system of the present invention, the ability to respond to customers and inventory risks can be quantitatively evaluated, and a parts inventory plan can be made based on the clear indicators. The inventory of parts can be set according to the degree of sharing, and the industrial use effect is extremely large.

Claims

This is a parts inventory planning support system for procurement of parts and components required for product production in anticipation of future product demand. Demand forecast information for each model, parts used for each model Inputting used component configuration information, inventory cost information for calculating costs incurred by having parts in stock, and product sales profit information for calculating product sales profit for each product;

A production amount provisional setting step for assuming the maximum production amount of each model, and a parts inventory amount setting step for setting the parts inventory amount according to the maximum production amount of each model provisionally set in the production amount provisional setting step. The inventory cost calculation step of calculating the expected value of the inventory cost for the inventory quantity set in the parts inventory quantity setting step from the demand forecast, and the product sales profit at the production quantity assumed in the production quantity provisional setting step above Product sales profit calculation step of calculating expected value from demand forecast, and cost trade-off evaluation step of performing cost trade-off evaluation from expected value of inventory cost and expected value of product sales profit at each production volume. Parts inventory planning support system.

This is a parts inventory planning support system for procuring parts or components required for product manufacturing in anticipation of future product demand. Demand forecast information for each model, demand forecast information for each model group, each model Used parts and component information of used parts, Inventory cost information for calculating the cost of having parts in stock, product sales profit information for calculating product sales profit for each product, and sales of products A step for entering product sales rules that define the criteria;

Assuming the production volume of the entire model group and the maximum production volume of individual models The production volume of the entire model group and the maximum production volume of individual models that were provisionally set in the production volume provisional setting step, and the parts inventory amount according to the degree of sharing between the parts. And the stock quantity set in the above-mentioned stock quantity setting step.

5) an inventory cost calculation step for calculating the expected value of inventory cost from the demand forecast, a product sales profit calculation step for calculating the expected value of the product sales profit at the assumed production volume from the demand forecast, and A parts inventory meter characterized by having a cost trade-off evaluation step of performing a cost trade-off evaluation from an expected value of inventory cost in terms of quantity and an expected value of product sales profit.

10 Planning support system.

3. A part inventory planning support method for procuring parts or components necessary for product production in anticipation of future product demand.

By having demand forecast information for each model, demand forecast information for model groups, used parts and used component configuration information for each model, and having parts in stock

! 5 Inventory cost information for calculating incurred costs, product sales profit information for calculating product sales profit for each product, and product sales rules that define product sales standards are used for the entire model group. Tentatively set the production volume of

The parts production quantity is set according to the production quantity of the whole model group temporarily set as described above, the maximum production quantity of 0 individual models, and the degree of commonality of each part between the models,

The expected value of the inventory cost at the set inventory amount is calculated from the demand forecast, and the expected value of the product sales profit at the assumed production volume is calculated from the demand forecast. A parts inventory planning support method characterized by displaying expected sales profit.

In the parts inventory planning support system described in claims 1 or 2 or 3,

Using demand forecast information for each model and the inventory of each part,

Select one model, calculate the maximum product production capacity assuming that models other than the model are in standard demand,

From the maximum amount of shared parts used in multiple models, calculate the correlation between the maximum producible quantities between multiple models,

A component inventory planning support system for displaying the maximum producible quantity and the maximum producible quantity correlation.

5. After the parts inventory planning support system according to any one of claims 1 to 4, set an appropriate inventory amount in advance, the parts procurement time determined by the procurement lead time of each part, and the parts procurement time A parts procurement instruction system characterized by automatically presenting the parts procurement amount based on the parts inventory amount and the order backlog.