JP2021068395A - Cost prediction method and cost prediction program - Google Patents

Abstract

To perform a more accurate cost prediction obtained by considering past results.SOLUTION: A model of a vehicle comprises a combination of various specifications, and components used for each specification are different. Information of specifications of each model of past manufactured products, components used in the specifications and the cost of each component is acquired in predicting each cost of a model of a new product. For example, a model 1 of a new product has components and a cost corresponding to specifications of sunroof existence, AT existence and navigation existence, and a model 2 has components and a cost corresponding to sunroof nonexistence, AT existence, navigation nonexistence, or the like. In this case, differences in specifications different between the models 1, 2 are extracted, and a balance amount of costs corresponding to the differences in the specifications is calculated. Then, respective costs of the models 1, 2 are estimated on the basis of the difference and the balance amount different between the models 1, 2 is estimated.SELECTED DRAWING: Figure 7

Description

The present invention relates to a cost prediction method and a cost prediction program for predicting the cost of a product.

When developing a new product that partially changes the specifications of an existing product, the manufacturing cost of the new product is predicted. In the early stages of development, it is difficult to make accurate predictions, so it is common to make rough predictions based on past experience.

As a prediction technique using a computer, it has been proposed to use past achievements. For example, for each part of a new product design plan, there is a technique of assigning the cost results of similar past designs and predicting the cost of the design plan based on the assigned cost results (for example, the following patent documents). See 1.).

Japanese Unexamined Patent Publication No. 2005-182558

When one specification is changed, it is necessary to change the other specifications accordingly. For example, in the design of an automobile, when a vehicle model with a sunroof is set as the basic vehicle model, not only the cost of a part called a sunroof is incurred, but also the cost of a dedicated harness may be required. With the technique disclosed in Patent Document 1, it is possible to predict the cost of the sunroof from past results, but the possibility that other parts need to be changed due to the installation of the sunroof is not considered. Therefore, the accuracy of the forecast cost is low.

In one aspect, it is an object of the present invention to be able to make more accurate cost predictions in light of past performance.

According to one aspect of the present invention, the cost prediction method is a cost prediction method for predicting the cost of a model in which a plurality of specifications of a product are changed, and includes an acquisition step for acquiring a past cost record of the product and a plurality of costs. The first cost actual, which is the cost when one of the above specifications is provided, and the second cost actual, which is the cost when one of the above specifications is not provided, are provided for each of the plurality of the above specifications. The extraction step to be extracted, the difference calculation step for calculating the difference between the plurality of first cost results and the plurality of second cost results for each of the specifications, and the information on the differences between the plurality of specifications. It is a requirement that the computer executes the process of the cost calculation step of calculating the cost of the model based on the difference.

According to one aspect of the present invention, there is an effect that more accurate cost prediction can be performed in view of past achievements.

FIG. 1 is a diagram illustrating a cost forecast of a product according to an embodiment. FIG. 2 is a functional block diagram of an information processing device that predicts the cost of a product according to an embodiment. FIG. 3 is a diagram showing a hardware configuration example of the information processing device according to the embodiment. FIG. 4 is a diagram showing an example of information used for cost prediction of the embodiment. (Part 1) FIG. 5 is a diagram showing an example of information used for cost prediction of the embodiment. (Part 2) FIG. 6 is a diagram showing an example of information used for cost prediction of the embodiment. (Part 3) FIG. 7 is a diagram showing an example of information used for cost prediction of the embodiment. (Part 4) FIG. 8 is a flowchart showing a processing example of cost prediction according to the embodiment. FIG. 9 is a diagram for explaining information on the combination of parts for each specification difference showing a plurality of combinations of parts used when a plurality of specifications are combined. FIG. 10 is another functional block diagram of the information processing apparatus that predicts the cost of the product according to the embodiment. FIG. 11 is a flowchart showing another processing example of cost prediction according to the embodiment. FIG. 12 is a diagram showing the results of multiple regression analysis based on information on the combination of parts for each specification difference according to the embodiment. FIG. 13 is a diagram showing a case where capital investment is required for cost estimation of the embodiment.

(Embodiment)
The disclosed cost prediction method and the embodiment of the cost prediction program will be described in detail with reference to the drawings below.

FIG. 1 is a diagram illustrating a cost forecast of a product according to an embodiment. The information processing device 100 calculates the cost for each specification of the new product based on the data of the past product at the time of development of the new product or the like. For example, the information processing apparatus 100 executes the cost prediction program P by a user operation (startup trigger input) such as when developing a new product, and performs cost prediction processing. For example, the cost prediction program P is stored in a storage unit such as a memory, and is read and executed by the processor.

The information processing apparatus 100 includes specifications-model information 111 indicating the relationship between specifications and models for each product manufactured in the past, model-part information 112 indicating the relationship between models and parts, and parts-cost information indicating the relationship between parts and costs. Each of 113 is stored and stored in a database (DB).

The model includes, for example, a basic model corresponding to the basic specifications of the product, and a plurality of derivative models (for example, models 1 to 4 described later) in which specifications for each function such as options are added to the basic model. The specifications consist of a combination of parts for each function. These plurality of derivative models correspond to each model of the new product (model 1 is the first product, model 2 is the second product, ...). The model-part information 112 indicates a combination of parts used for each model. The component-cost information 113 indicates the cost for each of a plurality of models to be developed as a new product.

Although details will be described later, the information processing device 100 is a new vehicle based on a database (DB) of specifications, models, parts, products, and costs of vehicles manufactured in the past, with the development of the new vehicle as a start trigger. Calculate the cost for each model.

The cost prediction program P executes processing according to the following procedure, for example. First, each DB is referred to and past information is collected (step S101). For example, the specifications-model information 111, model-parts information 112, and parts-cost information 113 of the past products stored in the DB are read out, a certain target model is extracted, and the specifications, parts, and costs of the target model are obtained. Link (associate).

For example, each specification of the vehicle includes a sunroof, a leather seat, an AT (automatic car), an MT (manual car), a navigation system (navigation), and the like. The model consists of different combinations of these specifications, and there are a plurality of models.

The combination of parts required for different specifications differs depending on the model. For example, model 1 (first product) consists of a combination of specifications consisting of a sunroof, navigation, and AT, and model 2 (second product) has specifications consisting of no sunroof, no navigation, no automatic braking, and AT. It consists of a combination. In this case, model 1 requires parts such as a sunroof, a sunroof roof, and a harness type A. On the other hand, in the model 2, the sunroof and the roof for the sunroof are not required, but the parts of the harness type B are required. In this way, the parts required between the models 1 and 2 differ depending on the model (referred to as the difference in specifications and the difference in specifications).

Next, based on each of the collected information, a combination of parts having different specifications is obtained (step S102). The specification difference is, for example, the presence / absence of the sunroof of the vehicle. Since different parts are used for each specification, the combination of parts used for each different specification is different. In this process, the specifications related to the target model and the related information of the parts are obtained.

Next, multiple regression analysis is performed by inputting a combination of parts that discriminate against specifications (step S103). A general-purpose processing technique can be used for the multiple regression analysis processing itself. For example, the cost difference information for each specification difference is calculated by performing the multiple regression analysis with the specification difference as the explanatory variable and the cost of the component as the objective variable. To do.

Finally, based on the cost difference information for each specification difference, the cost when the specifications for each model are changed is calculated by simulation (step S104). As a result, when developing a new product, the cost for each model is calculated based on the past product information and presented to the user as a cost simulation screen.

Here, regarding the outline of the cost forecast for each model in the embodiment, the conventional problems and the improvement points by the embodiment will be described. In general, when the number of product models increases, the cost of product development increases and the development period increases. In the conventional technology, the specification table for each model of the product is managed, the cost, sales and profit of each model of the product are assumed, and each model of the product to be developed is determined by confirming the profitability.

However, with the conventional technology, the method of estimating the cost of each model of a product from the combination of the product specifications may require an unexpected investment, and the final required cost cannot be predicted accurately. There was a problem. As a result, some of the models of the products on sale may not be profitable.

In the embodiment, in order to solve the above-mentioned problem in the past, when the number of models increases by learning the pattern in which investment is generated from the specifications for each model in the past and the actual cost, and cost-simulating the learning pattern. Improve the accuracy of cost forecasts.

Here, in the embodiment, the cost is assumed not only for "parts directly related to the product specification change" but also for "parts secondarily related to the product specification change". As for "parts directly related to product specification change", for example, it is assumed that the product specifications of the vehicle have a sunroof / no sunroof, and the parts have a sunroof purchase / no sunroof purchase.

In addition, "parts that are secondary to product specification changes" include, for example, each part when the product specifications are with or without sunroof.
Purchase a perforated roof for the sunroof / Purchase a roof without holes for the sunroof Purchase a harness with wiring to the sunroof / Purchase a harness without wiring to the sunroof Purchase 8 clips to stop the harness / Purchase 4 clips to stop the harness.

By simulating the cost of each model, the embodiment makes it possible to easily extract and exclude unprofitable models and improve the profitability of the entire product life cycle. In addition, it is more difficult to predict manually as many product production bases and sales bases are deployed globally, but according to the embodiment, the cost for each model can be easily and accurately obtained. ..

FIG. 2 is a functional block diagram of an information processing device that predicts the cost of a product according to an embodiment. The information processing device 100 has the functions of the past information collecting unit 201, the specification difference-part combination unit 202, the multiple regression analysis unit 203, and the new product cost simulation unit 204 as the control unit 200. It also includes a function of generating a cost simulation screen and displaying and outputting it to the display unit 205.

Further, the storage unit (DB) 211 of the specification-model information 111 shown in FIG. 1, the DB 212 of the model-part information 112, and the DB 213 of the part-cost information 113 are included. It also includes DB221 from which specification-model-parts-cost information is extracted, DB222 of specification-parts related information, and DB223 of cost difference information for each specification difference.

The past information collecting unit 201 refers to the specifications of the products manufactured in the past-DB211 of the model information 111, the DB212 of the model-parts information 112, and the DB213 of the parts-cost information 113, and the specifications, models, and parts for each model. And the cost are linked (associated). Then, the result of the association is stored in the DB221 of the specification-model-part-cost information.

The specification difference-part combination unit 202 refers to the specification-model-part-cost information DB221 corresponding to the target model, obtains the specification-discrimination specification-part related information, and stores it in the DB 222.

The multiple regression analysis unit 203 refers to DB 222 of the specification-part related information, performs multiple regression analysis with the specification difference as the explanatory variable and the cost of the component as the objective variable, and stores the cost difference information for each specification difference in DB 223. ..

The new product cost simulation unit 204 refers to the DB 223 of the cost difference information for each specification difference, calculates the cost when the specifications for each model are changed, and displays and outputs the cost simulation screen to the display unit 205.

FIG. 3 is a diagram showing a hardware configuration example of the information processing device according to the embodiment. The information processing device 100 can be configured by, for example, a general-purpose PC or server composed of the hardware shown in FIG.

The information processing device 100 includes a CPU (Central Processing Unit) 301, a memory 302, a network interface (IF) 303, a recording medium IF 304, a recording medium 305, an input / output IF 306, and the like. Reference numeral 300 denotes a bus connecting each part.

The CPU 301 is an arithmetic processing device that functions as a control unit that controls the entire information processing device 100. The memory 302 includes a non-volatile memory and a volatile memory. The non-volatile memory is, for example, a ROM (Read Only Memory) for storing the program of the CPU 301. The volatile memory is, for example, a DRAM (Dynamic Random Access Memory) used as a work area of the CPU 301, a SRAM (Static Random Access Memory), or the like.

The network IF 303 is a communication interface for a network NW such as a LAN (Local Area Network), a WAN (Wide Area Network), and the Internet. The information processing device 100 communicates with the network NW via the network IF 303 and communicates with the external device. For example, it is possible to communicate with a terminal operated by a user.

The recording medium IF 304 is an interface for reading and writing the information processed by the CPU 301 with and from the recording medium 305. The recording medium 305 is a recording device that assists the memory 302, and an HDD (Hard Disk Drive), an SSD (Solid State Drive), a USB (Universal Serial Bus) flash drive, or the like can be used.

By executing the program recorded in the memory 302 or the recording medium 305 by the CPU 301, each function of the control unit of the information processing apparatus 100 (past information collection unit 201 to new product cost simulation unit 204 in FIG. 2) is realized. Can be done. Further, the memory 302 and the recording medium 305 record and hold the information handled by the information processing apparatus 100. For example, the memory 302 and the recording medium 305 are the above-mentioned DBs (DB 211 of specification-model information 111, DB 213 of component-cost information 113, DB 221 of specification-model-component-cost information, and cost difference information for each specification difference. It holds the information of DB223 etc.).

The input / output IF 306 is an interface for transmitting / receiving information to / from the outside of the information processing device 100. An input unit such as a keyboard and a mouse, a display unit such as a display, and the like are connected to the input / output IF 306, and data input by user operation, screen display output to the user, and the like can be performed. User-operated data input / output can also be performed with an external terminal connected via the NW. In this case, the display unit 205 of FIG. 2 can use a display provided on the terminal. , You can input operations via the keyboard of the terminal.

4 to 7 are diagrams showing an example of information used for cost prediction of the embodiment. Each information example when the product example is a vehicle will be described. 4A and 4B show specifications and a list of parts for each model, FIG. 4A shows an example of specifications for each model, and FIG. 4B shows an example of a list of parts for each model.

Each piece of information in FIG. 4 is information on specifications and parts for each model of a vehicle manufactured in the past, and the control unit (past information collecting unit 201) has DB211 of specifications-model information 111 and DB212 of model-parts information 112. , Parts-Information is acquired from DB 213 of cost information 113.

In the specification list 400 for each model in FIG. 4A, the control unit (past information collecting unit 201) refers to the DB 211 of the specification-model information 111 and extracts the specifications for each of the models 1 to 4 (the specifications for each model 1 to 4 are extracted). Specifications used in each model with a circle). For example, model 1 is a specification with a sunroof, AT, navigation, and anti-lock brake. Model 2 has leather seats, AT, automatic braking, and anti-lock braking.

More specifically, each of the models 1 to 4 is an increase / decrease (yes / no) of each specification mounted on the vehicle. For example, each model 1 to 4 corresponds to a derivative model in which optional specifications are added to the basic specifications of the basic model of the vehicle.

The parts list 410 for each model (combination of specifications) in FIG. 4 (b) includes model-specification information and model-part information 112 extracted by the control unit (past information collecting unit 201) in FIG. 4 (a). It is extracted by referring to the DB 212 of the above and the DB 213 of the component-cost information 113.

For example, the component list 411 used in the specifications of the model 1 extracts each component such as a sunroof, a roof for mounting a sunroof (roof A), a harness A, and a handle, and the cost of each component. In addition, the total cost of all the parts used in the model 1 (for example, the price such as the amount of money) is obtained. Similarly, a list of parts 421 to 413 of models 2 to 4 is obtained. The information of the specification list 400 for each model 1 to 4 and the information of the part list 410 for each model are stored in the DB 221 of the specification-model-part-cost information.

FIG. 5 shows information related to the combination of parts for each specification difference, and the control unit (specification difference-part combination unit 202) refers to DB221 of specification-model-part-cost information, and is shown in FIG. 4A. Information on the specification list 400 for each model and the parts list 410 for each model is acquired. Then, the control unit (specification difference-part combination unit 202) requests the combination of parts having different specifications, and stores the information 500 of the combination of parts for each specification difference in the DB 222.

For example, the combination of parts used when the product specification is "with sunroof" and "without sunroof" is extracted. Here, the parts used in "with sunroof" are "sunroof (23,000)", "roof A (8,000)", and "harness A (689)". The numbers in parentheses indicate the cost of each part (first cost actual).

The parts used in "without sunroof" are "without sunroof (0)", "roof B (6,800)", and "harness B (612)" (second cost record). At this time, the control unit (specification difference-part combination unit 202) can acquire the cost for each part to be used, and learns the price difference (difference between the first cost actual and the second cost actual) corresponding to the specification difference. ..

FIG. 6 shows cost difference information 600 for each specification difference. The cost difference indicates the difference between the presence / absence of each specification. The control unit (multiple regression analysis unit 203) refers to the information (FIG. 5) of the combination of the parts having the specification discrimination stored in the DB 222 of the specification-part related information, and sets the specification difference as an explanatory variable and the cost of the part. Perform multiple regression analysis as the objective variable. As a result, the cost difference information 600 for each specification difference is obtained and stored in the DB 223.

In the example of FIG. 6, for example, the difference between the case where all the parts used in the specification "sunroof" are "yes" and the case where all the parts are "no" is calculated as "30,000". Further, the difference between the case where all the parts used for the specification "anti-lock brake" are "yes" and the case where all the parts are "no" is calculated as "2782".

FIG. 7 shows a list of specifications for each model and cost information 700. The control unit (new product cost simulation unit 204) refers to DB223 of the cost difference information 600 for each specification difference, and calculates the cost when the specifications for each model are changed by simulation. The information in the specification list for each model is the same as in FIG. 4 (a) (marked with ◯ is the specification used in each model). For example, the cost indicates the estimated price of one vehicle.

As a result of the simulation, for example, according to the specifications of the model 1 (with sunroof, AT, navigation, and anti-lock brake), the cost is calculated to be 283200. According to the model 2 specifications (with leather seat, AT, automatic brake, anti-lock brake), the cost is calculated to be 190,800.

The specification list for each model and the cost information 700 shown in FIG. 7 are output to the display unit 205. This allows the user to easily compare the specifications and costs of different models 1 to 4. As described above, each of the models 1 to 4 is an increase / decrease (with / without) of each specification mounted on the vehicle.

The control unit (new product cost simulation unit 204) learns the cost increase / decrease according to the increase / decrease in the specifications based on the results of the past products (specifications for each model 1 to 4 of the vehicle (see FIG. 4 (a))). Then, the cost for each model is estimated by simulation.

FIG. 8 is a flowchart showing a processing example of cost prediction according to the embodiment. The contents to be executed by the control unit (CPU301) of the information processing apparatus 100 are shown. The control unit starts executing the following processes by user operation (startup trigger input) such as when developing a new product.

First, the control unit acquires information on the past product list (step S801). Here, the control unit uses specifications-model information 111 indicating the relationship between specifications and models for each product manufactured in the past, model-part information 112 indicating the relationship between models and parts, and parts-costs indicating the relationship between parts and costs. Information 113 is stored in DB 221 of specification-model-part-cost information.

Next, the control unit loops each process of steps S802 to S810 for one product (new product). In step S803, the model of each product is acquired (step S803). The model has a plurality of models (for example, models 1 to 4) as a new product.

Next, the control unit loops each process of step S804 to step S809 for a certain model. In step S805, the specifications for each model are acquired (step S805). For example, the control unit acquires the specification-model information 111 stored in the DB 221 of the specification-model-part-cost information (corresponding to the specification list 400 for each model in FIG. 4A).

Next, the control unit acquires the parts in each model and their costs (step S806). For example, the control unit acquires the part-cost information 113 stored in the DB 221 of the specification-model-part-cost information (corresponding to the part list 410 for each model in FIG. 4B).

Next, the control unit creates a combination of the specification difference and the component difference (step S807). For example, the control unit uses the acquired specification-model information 111 and part-cost information 113 to determine the specification difference (difference between presence / absence of specifications) for each model and the part difference for specification discrimination for parts used in different specifications. Create a combination with (difference between presence / absence of parts with different specifications). This combination is created for all possible patterns. Then, the control unit saves the created component combination information (corresponding to FIG. 5) for each specification difference in DB 222 as specification-component related information (step S808).

After that, the control unit determines the end of processing related to one model in step S809, returns to the processing of step S804 if the processing of another model of a certain product remains, and steps if the processing related to one model is completed. The process proceeds to S810. After that, the control unit determines the end of processing related to one product in step S810, returns to the processing of step S802 if there is another target product related to a certain product, and returns to the processing of step S802 if the processing related to the product is completed. Move to processing.

In step S811, the control unit infers the cost difference for each specification difference. For example, the control unit performs multiple regression analysis using the component combination information (FIG. 5) for each specification difference created in step S808, with the specification difference as an explanatory variable and the cost of the component as an objective variable. As a result, the cost difference information 600 (corresponding to FIG. 6) for each specification difference is obtained and stored in the DB 223.

Next, the control unit acquires the specification difference for each model of the new product (step S812). For example, the control unit refers to the DB 223 of the cost difference information for each specification difference, and acquires the specification difference for each model of the new product. Then, the control unit estimates the cost for each specification indicated by the specification difference (step S813). The estimated cost for each specification is generated as a cost simulation screen (corresponding to FIG. 7) and displayed and output to the display unit 205. With the above, a series of processing is completed.

By the above processing, the information processing apparatus 100 presents to the user, for example, a cost according to an increase or decrease in the specifications shown by the models 1 to 4 of the product. The user can easily select a profitable model (for example, models 2 and 4 in FIG. 7) based on the presented cost for each model. Similarly, it becomes possible to determine an unprofitable model (for example, models 1 and 3 in FIG. 7). In addition to the presented cost, the optimum model can be selected in consideration of the past performance (sales and sales) of the corresponding model.

(Other calculation examples of cost estimation)
In the above explanation, an example of cost estimation based on the specification difference is used. Since factors other than the specification difference may be a cost factor, it is possible to perform not only the cost estimation based on the specification difference but also the cost estimation based on the combination of specifications. Furthermore, it is possible to carry out capital investment in which factors other than specifications are cost factors, and cost estimation based on information such as material costs, exchange rates, regions, and suppliers.

FIG. 9 is a diagram for explaining information on the combination of parts for each specification difference showing a plurality of combinations of parts used when a plurality of specifications are combined. As the information 900 of the combination of parts for each specification difference, for example, it is shown that a large number of combinations occur in the parts used in one specification "with / without sunroof" and another specification "with / without navigation".

For example, if there is only one specification "with / without sunroof", it is easy to select (specify) the parts to be used as shown in FIG. However, when one specification "with / without sunroof" and another specification "with / without navigation" are combined, the combination of parts to be used increases.

Explaining with the example of FIG. 9, when focusing on only one specification “with sunroof”, the parts used are “harness A” or “harness C”. Further, when focusing only on the other one specification "without sunroof", the parts used are "harness B" or "harness D". Here, when focusing only on the other one specification "with navigation", the parts used are "harness A" or "harness D". Further, when focusing only on one other specification "no navigation", the parts used are "harness B" or "harness C".

According to the above combination, in the case of "with sunroof" and "with navigation", the part to be used is "harness A". In the case of "without sunroof" and "without navigation", the part used is "harness B". In the case of "with sunroof" and "without navigation", the part used is "harness C". In the case of "without sunroof" and "with navigation", the part used is "harness D". In FIG. 9, the costs for each part (harnesses A to D) are also shown. In this way, the cost is not determined by only one specification, and the cost varies depending on the parts used in a plurality of specifications. The control unit (specification difference-part combination unit 202) obtains a combination of parts having different specifications, and stores the combination of parts for each specification difference in DB 222 of the related information of the specifications-parts.

Here, although only the combination of the two specifications is shown in FIG. 9, each model is composed of a combination of a large number of specifications as described in FIG. 4, and the parts used in a larger number of specifications than in FIG. 9 Selection becomes difficult. For example, there are about 500 types of vehicle harnesses, and the more combinations of specifications there are, the more the accuracy of cost prediction will be significantly reduced.

FIG. 9 shows a state in which each specification is not independent. For this reason, the cost change due to the combination of a plurality of specifications may be extremely large than the cost change with or without individual specifications for a specific component. In this respect, the accuracy of prediction cannot be improved even if the specifications are used as parameters and the cost is used as a result for multivariate analysis as in the conventional case.

In order to deal with this, the information processing apparatus 100 performs an analysis using the specification set as a variable. For example, it is possible to extract from past data which specification change causes a cost change for each part, and to determine the change in each specification and the cost change of the target part, not only the cost change with or without a simple specification, but also multiple specifications. Consider the cost change of the combination of. For the combination of specifications at this time, the amount of cost change is analyzed for all possible patterns.

In addition, if factors other than specifications, such as molds and manufacturing equipment, are required when the specifications are changed, the investment cost will be reflected in the cost of the parts, resulting in extremely large cost changes. May be. In this case, the cost is estimated using the information on capital investment. For example, it is possible to extract which specification change for each part requires investment for each production base (for example, in-house factory or supplier of purchased parts) and classify it according to investment pattern (die, processing equipment, etc.). ). Then, the specification change and its cost change (amortization of investment to parts price) are analyzed from the data acquired in the past for each part, production base, and investment pattern.

Furthermore, factors other than specifications for cost fluctuations include, for example, material cost fluctuations, exchange fluctuations, regional correction values, supplier correction values, etc., and cost estimation by adding these parameters that affect specification differences and cost differences. The accuracy can be further improved. In this way, the cost of each model of the product can be estimated based on the cost estimation by adding information such as the specification set, capital investment, and material cost fluctuation based on the specification difference.

FIG. 10 is another functional block diagram of the information processing apparatus that predicts the cost of the product according to the embodiment. In FIG. 10, the same functional parts as those in FIG. 2 are designated by the same reference numerals. In FIG. 10, in addition to the basic functions shown in FIG. 2, the functions of the specification combination creation unit 1001 and the capital investment creation unit 1002 are provided.

The specification combination creation unit 1001 uses a plurality of combinations of parts when a plurality of specifications are combined based on the specification-discriminatory part combination information (FIG. 5) stored in the specification-part related information DB 222. Information 900 (see FIG. 9) of the combination of parts for each specification difference indicating the above is created. Then, the specification combination creation unit 1001 requests the multiple regression analysis unit 203 to perform the analysis again using the specification combination as a new explanatory variable.

Based on the cost estimation result of the new product cost simulation unit 204, the capital investment creation unit 1002 excludes the model whose cost has increased due to the need for capital investment for new product manufacturing from the cost estimation process, and performs multiple regression analysis. To execute. In addition, the cost is calculated by multiplying it by a predetermined investment coefficient and executing a simulation.

FIG. 11 is a flowchart showing another processing example of cost prediction according to the embodiment. An example of processing based on each function of the information processing apparatus 100 shown in FIG. 10 is shown. Here, steps S1101 to S1113 in FIG. 11 correspond to the cost estimation process (steps S801 to S813 in FIG. 8) based on the above-mentioned specification difference. In the process of FIG. 11, the processes of steps S1114 to S1117 are added.

The control unit (CPU301) of the information processing apparatus 100 determines whether or not there is a non-independent variable after executing the processes of steps S1101 to S1113 (step S1114). When there are a plurality of combinations for each specification difference (step S1114: Yes) as in the above-mentioned information 900 for the combination of parts for each specification difference, the control unit sets the combination of specifications as a new variable (step S1115). The process of step S1111 is executed again.

When the non-independent variables disappear and the processing of the plurality of combinations for each specification difference is completed (step S1114: No), the control unit determines whether there is an investment generation pattern such as the need for new capital investment (step S1116). .. If there is no investment generation pattern (step S1116: No), the above processing is terminated. On the other hand, if there is an investment generation pattern (step S1116: Yes), the control unit sets a necessary investment case (step S1117). After that, the control unit excludes the case (model) in which the cost of new capital investment is required to be more than a predetermined amount as a result of the set investment case from the processing of the multiple regression analysis in step S1111. In cases other than exclusion, the simulation is executed by multiplying the cost by the predetermined investment coefficient corresponding to the cost calculation and the additional investment in step S1113.

FIG. 12 is a diagram showing the results of multiple regression analysis based on information on the combination of parts for each specification difference according to the embodiment. As shown in FIG. 12A, as the cost of specification discrimination, the respective costs of “with / without sunroof” and “with / without navigation” and significance information are calculated. After that, the control unit performs multiple regression analysis using the combination of “with / without sunroof” and “with / without navigation” as explanatory variables. As a result, as shown in FIG. 12B, cost and significance information is calculated for each combination of the specifications of "with sunroof without navigation", "with sunroof without navigation", and "with sunroof with navigation". To.

For example, when the superiority (predetermined value) after the combination of the specifications is improved as compared with that before the combination of the specifications, the control unit decides to adopt this combination of specifications. In the example of FIG. 12, all the advantages of the combination of specifications will be adopted.

FIG. 13 is a diagram showing a case where capital investment is required for cost estimation of the embodiment. The original data before the processing by the multiple regression analysis unit 203 includes, for example, information on each model (corresponding to a model), part number (manufacturing department, etc.), production factory, supplier, and cost of the product.

In the example shown in FIG. 13, in the list, for example, when the model "B" and the production factory "Brazil" are used for production, the cost (price, etc.) "83,000" is more than the specified value (price). Is in a state of being. In this case, the control unit excludes this model "B" (production factory "Brazil") from the processing target of the multiple regression analysis unit 203 based on the large amount of capital investment. In cases other than exclusion, when the cost is calculated by the new product cost simulation unit 204, a simulation multiplied by a predetermined investment coefficient is executed according to the degree of additional investment.

By the above processing, the information processing apparatus 100 is based on, for example, cost estimation based on the specification difference of each model of the product, combination of a plurality of specifications, and the occurrence of additional investment, for example, information on capital investment. The cost can be estimated and presented to the user. Furthermore, as information on the occurrence of additional investment, cost estimation can be performed based on information such as material cost fluctuations, exchange fluctuations, regional correction values, and supplier correction values related to the products to be manufactured. By combining these, the cost for each model at the time of new product development can be estimated more accurately.

Here, the specifications and costs for each model of the target new product may not be obtained from the actual cost of the past products. For example, the cost of parts incurred when a specification is added to a model of a new product may not be included in the past cost performance. This is because some models may or may not have additional components. Correspondingly, in the embodiment, for example, when additional parts are generated in 50% of the past products, the average amount of the cost of each product is used in the calculation of the simulation. For example, it is possible to add 50% of the cost of additional parts to the cost of a new product. As described above, in the embodiment, the cost can be predicted in consideration of the amount of additional parts and the occurrence rate caused by the model change.

According to the cost prediction of the embodiment described above, when predicting the cost of a model in which a plurality of specifications of the product are changed, an acquisition step of acquiring the actual cost of the past product is performed. After that, the first cost record, which is the cost when one of the multiple specifications is provided, and the second cost record, which is the cost when one specification is not provided, are calculated for each of the plurality of specifications. Perform the extraction step to extract. After that, a difference calculation step for calculating the difference between the plurality of first cost actual results and the plurality of second cost actual results is performed for each specification. After that, a cost calculation step of calculating the cost of the model is performed based on the difference information and the difference amount of different specifications for each of the plurality of models. As a result, the cost of the model can be calculated accurately by obtaining the difference in cost depending on the presence or absence of specifications (specification difference) for each specification.

Further, in the acquisition step, as the past cost results of the product, the specification information 111 of the model, the component information 112 used in the model, and the component cost information 113 are acquired. Then, the extraction step is processed based on the information associated with the specifications for each model, the parts, and the cost. As a result, the cost of the model of the new product can be calculated only by using the actual cost of the past product, and the cost can be calculated without information such as other parameters.

Further, in the difference calculation step, multiple regression analysis is performed using the information on the difference between the presence and absence of specifications as the explanatory variable and the cost as the objective variable, and the difference for each different specification is calculated. This makes it possible to easily calculate the difference between different specifications using multiple regression analysis.

Further, in the cost calculation step, the cost when the specifications for each model are changed is calculated by simulation using the difference for each different specifications. This makes it easy to calculate the cost for each model.

In addition to the above, it may further include a specification combination step that creates a combination of specifications. In the specification combination step, the first cost actual, which is the cost when having one specification, and the second cost actual, which is the cost when not having one specification, are modeled, which are extracted in the extraction step. Combine multiple specifications of. Then, the combination is used as a new explanatory variable to process the difference calculation step. As a result, the cost can be calculated for each of a plurality of models having different combinations of a plurality of specifications. For example, it is possible to deal with different parts used in each of a plurality of combinations with / without sunroof and with / without navigation, and it becomes possible to accurately calculate the cost for each model from the parts used.

In addition, among the past cost results, a combination that causes a cost increase of a predetermined value or more when a plurality of parts are added is specified in advance, and in the cost calculation step, if a combination exists, the combination is specified. The cost may be calculated including the increase in the cost corresponding to. Depending on the model, it may be necessary to add new parts or the like, and even in such a case, the cost can be calculated accurately corresponding to the additional parts.

Further, in the difference calculation step, the processing for the model including the information in which the cost of a predetermined value or more is generated may be excluded from the model. As a result, it is known that the cost will be high in advance, and the processing can be made more efficient without performing the processing (multiple regression analysis) for the model that does not require analysis.

Further, in the cost calculation step, the cost may be calculated by using a coefficient according to the information of additional cost generation in the model. In addition, information on additional cost incurred includes information on capital investment, material cost fluctuations, exchange fluctuations, regional correction values, and supplier correction values required for manufacturing products. This makes it possible to calculate the cost more accurately.

From these facts, according to the embodiment, it becomes possible to make a more accurate cost prediction in view of the past results, and when one specification is changed, the other specifications are changed accordingly. It can be handled when it is necessary to do so. For example, in the design of an automobile, when a model B with a sunroof is newly set as a basic model A, not only the cost of a part called a sunroof is incurred in the model B, but also the cost of a dedicated harness is required. In this regard, according to the embodiment, the cost of the sunroof and the cost when other parts are changed due to the installation of the sunroof can be calculated from the past results, and the cost can be calculated accurately even with the new model B. Will be. If the same model C has no sunroof and has navigation, even if a different harness is required, the cost of model C can be calculated accurately by combining the information on the specification difference between the presence or absence of sunroof and the presence or absence of navigation from past results. ..

The cost prediction method described in the embodiment of the present invention can be realized by causing a processor such as a server to execute a program prepared in advance. This cost prediction method is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM (Compact Disc-Read Only Memory), a DVD (Digital Versaille Disk), or a flash memory, and is recorded by the computer from the recording medium. It is executed by being read. It may also be distributed via a network such as the Internet.

The following additional notes are further disclosed with respect to the above-described embodiment.

(Appendix 1) A cost prediction method for predicting the cost of a model of a product whose specifications have been changed.
Acquisition steps to acquire past product cost performance,
The first cost record, which is the cost when one of the plurality of specifications is provided, and the second cost record, which is the cost when one of the specifications is not provided, are obtained from each of the plurality of specifications. Extraction steps to extract with
A difference calculation step for calculating the difference between the plurality of first cost results and the plurality of second cost results for each of the specifications.
A cost calculation step of calculating the cost of the model based on the information of the difference of the plurality of specifications and the difference amount.
A cost prediction method characterized by a computer performing the processing of.

(Appendix 2) In the acquisition step, information on the specifications of the model, information on the parts used in the model, and information on the cost of the parts are acquired as the actual cost of the past product.
The extraction step is processed based on the information associated with the specifications of the model, the parts, and the cost.
The cost prediction method according to Appendix 1, wherein the cost is predicted.

(Appendix 3) In the difference calculation step, multiple regression analysis is performed using the difference information of the presence or absence of the specifications as the explanatory variable and the cost as the objective variable, and the difference for each different specification is calculated.
The cost prediction method according to Appendix 1 or 2, characterized in that.

(Appendix 4) In the cost calculation step, the cost when the specifications are changed for each model is calculated by simulation using the difference for each of the different specifications.
The cost prediction method according to any one of Supplementary notes 1 to 3, wherein the cost prediction method is described.

(Appendix 5) Further, a specification combination step for creating a combination of the above specifications is included.
In the specification combination step, the first cost actual, which is the cost when one of the specifications is possessed, and the second cost actual, which is the cost when one of the specifications is not possessed, are extracted in the extraction step. And, a combination of multiple specifications of the model,
The difference calculation step is processed using the combination as a new explanatory variable.
The cost prediction method according to Appendix 3 or 4, characterized in that.

(Appendix 6) Among the past actual cost results, a combination that increases the cost by a predetermined value or more when a plurality of parts are added is specified in advance.
In the cost calculation step, if the combination exists, the cost is calculated including the cost increase corresponding to the combination.
The cost prediction method according to any one of Supplementary note 1 to 5, wherein the cost prediction method is described.

(Appendix 7) In the difference calculation step, processing for a model including information in which a predetermined cost or more is generated is excluded from the model.
The cost prediction method according to Appendix 6, wherein the cost is predicted.

(Appendix 8) In the cost calculation step, the cost is calculated by using the coefficient corresponding to the information of the additional cost generation in the model.
The cost prediction method according to Appendix 6, wherein the cost is predicted.

(Appendix 9) The additional cost generation information includes information on capital investment, material cost fluctuations, exchange fluctuations, regional correction values, and supplier correction values required for manufacturing the product. The cost prediction method described.

(Appendix 10) A cost prediction program that predicts the cost of a model of a product whose specifications have been changed.
Acquisition steps to acquire past product cost performance,
The first cost record, which is the cost when one of the plurality of specifications is provided, and the second cost record, which is the cost when one of the specifications is not provided, are obtained from each of the plurality of specifications. Extraction steps to extract with
A difference calculation step for calculating the difference between the plurality of first cost results and the plurality of second cost results for each of the specifications.
A cost calculation step of calculating the cost of the model based on the information of the difference of the plurality of specifications and the difference amount.
A cost forecasting program characterized by having a computer perform the processing of.

100 Information processing device 111 Specifications-Model information 112 Model-Parts information 113 Parts-Cost information 200 Control unit 201 Past information collection unit 202 Specification difference-Part combination unit 203 Multiple regression analysis unit 204 New product cost simulation unit 205 Display unit 211, 212,213,221,222,223 DB
301 CPU
302 Memory 303 Network IF
305 Recording medium 400 List of specifications by model 410 List of parts by model 500 Information on combination of parts for each specification difference 600 Information on cost difference for each specification difference 700 List of specifications and cost information for each model 900 Combination of parts for each specification difference Information 1001 Specifications Combination Creation Department 1002 Capital Investment Creation Department NW Network P Cost Forecast Program

Claims (9)

It is a cost prediction method that predicts the cost of a model with multiple specifications changed in a product.
Acquisition steps to acquire past product cost performance,
The first cost record, which is the cost when one of the plurality of specifications is provided, and the second cost record, which is the cost when one of the specifications is not provided, are obtained from each of the plurality of specifications. Extraction steps to extract with
A difference calculation step for calculating the difference between the plurality of first cost results and the plurality of second cost results for each of the specifications.
A cost calculation step of calculating the cost of the model based on the information of the difference of the plurality of specifications and the difference amount.
A cost prediction method characterized by a computer performing the processing of. In the acquisition step, information on the specifications of the model, information on the parts used in the model, and information on the cost of the parts are acquired as the actual cost of the past product.
The extraction step is processed based on the information associated with the specifications of the model, the parts, and the cost.
The cost prediction method according to claim 1. In the difference calculation step, multiple regression analysis is performed using the information on the difference between the presence and absence of the specifications as the explanatory variable and the cost as the objective variable, and the difference for each of the different specifications is calculated.
The cost prediction method according to claim 1 or 2. In the cost calculation step, the cost when the specifications are changed for each model is calculated by simulation using the difference for each of the different specifications.
The cost prediction method according to any one of claims 1 to 3. In addition, it includes a specification combination step that creates a combination of the specifications.
In the specification combination step, the first cost actual, which is the cost when one of the specifications is possessed, and the second cost actual, which is the cost when one of the specifications is not possessed, are extracted in the extraction step. And, a combination of multiple specifications of the model,
The difference calculation step is processed using the combination as a new explanatory variable.
The cost prediction method according to claim 3 or 4. In advance, among the past cost results, a combination that causes a cost increase of a predetermined value or more when a plurality of parts are added is specified.
In the cost calculation step, if the combination exists, the cost is calculated including the cost increase corresponding to the combination.
The cost prediction method according to any one of claims 1 to 5, wherein the cost prediction method is characterized. In the difference calculation step, processing for a model including information that incurs a predetermined cost or more is excluded from the model.
The cost prediction method according to claim 6. In the cost calculation step, the cost is calculated by using the coefficient according to the information of the additional cost generation in the model.
The cost prediction method according to claim 6. It is a cost prediction program that predicts the cost of a model that changes multiple specifications of a product.
Acquisition steps to acquire past product cost performance,
The first cost record, which is the cost when one of the plurality of specifications is provided, and the second cost record, which is the cost when one of the specifications is not provided, are obtained from each of the plurality of specifications. Extraction steps to extract with
A difference calculation step for calculating the difference between the plurality of first cost results and the plurality of second cost results for each of the specifications.
A cost calculation step of calculating the cost of the model based on the information of the difference of the plurality of specifications and the difference amount.
A cost forecasting program characterized by having a computer perform the processing of.

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