Product cost modeling method and system

• US 20070118487A1
• Filed: 11/18/2005
• Published: 05/24/2007
• Est. Priority Date: 11/18/2005
• Status: Abandoned Application

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Seskin, Michael, Grichnik, Anthony
• Application Number:
  US11/281,748
• Publication Number:
  US 20070118487A1
• Time in Patent Office: Days
• US Class Current: 705/400
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

A method is provided for a product cost modeling system. The method may include establishing a product cost process model indicative of interrelationships between product costs of one or more existing products and a respective plurality of characteristic parameters of the existing products. The method may also include obtaining a set of values corresponding to a plurality of characteristic parameters of a target product from a data source and calculating the product cost of the target product based upon the set of values corresponding to the plurality of characteristic parameters of the target product and the product cost process model.

Claims

1. A method for a product cost modeling system, comprising:

establishing a product cost process model indicative of interrelationships between product costs of one or more existing products and a respective plurality of characteristic parameters of the existing products;

obtaining a set of values corresponding to a plurality of characteristic parameters of a target product from a data source; and

calculating the product cost of the target product based upon the set of values corresponding to the plurality of characteristic parameters of the target product and the product cost process model.

2. The method according to claim 1, further including:

presenting the product cost of the target product to the data source.

3. The method according to claim 1, further including:

determining a desired product cost range of the target product; and

modifying the plurality of characteristic parameters of the target product simultaneously such that an actual product cost of the target product is within the desired product cost range.

4. The method according to claim 1, wherein the data source is a computer aided design (CAD) environment.

5. The method according to claim 1, wherein the plurality of characteristic parameters include product attribute parameters indicative of physical attributes of the target product.

6. The method according to claim 1, wherein the plurality of characteristic parameters include file descriptive parameters indicative of file attributes of a CAD file corresponding to the target product.

7. The method according to claim 1, wherein the plurality of characteristic parameters include both product attribute parameters indicative of physical attributes of the target product and file descriptive parameters indicative of file attributes of a CAD file corresponding to the target product.

8. The method according to claim 1, wherein the establishing includes:

obtaining data records associated with the product costs of the products and characteristic variables of the products;

selecting the plurality of characteristic parameters from the characteristic variables;

generating a computational model indicative of the interrelationships between product costs of one or more products and a respective plurality of characteristic parameters of the products;

determining desired statistical distributions of the plurality of characteristic parameters of the computational model; and

recalibrating the plurality of characteristic parameters based on the desired statistical distributions.

9. The method according to claim 8, wherein selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the plurality of characteristic parameters from the characteristic variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

10. The method according to claim 8, wherein generating further includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

11. The method according to claim 8, wherein determining further includes:

determining a candidate set of the characteristic parameters with a maximum zeta statistic using a genetic algorithm; and

determining the desired distributions of the characteristic parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.

12. A computer system, comprising:

a database containing data records associating product costs of one or more existing products and a respective plurality of characteristic parameters of the existing products;

a data source; and

a processor configured to;

establish a product cost process model indicative of interrelationships between the product costs and the plurality of characteristic parameters of the existing products;

obtain a set of values corresponding to a plurality of characteristic parameters of a target product from the data source;

calculate the product cost of the target product based upon the set of values corresponding to the plurality of characteristic parameters of the target product and the product cost process model;

present the product cost of the target product to the data source;

determine a desired product cost range of the target product; and

modify the plurality of characteristic parameters of the target product simultaneously such that an actual product cost of the target product is within the desired product cost range.

13. The computer system according to claim 12, wherein the data source is a computer aided design (CAD) environment.

14. The method according to claim 12, wherein the data records include at least one of product attribute parameters indicative of physical attributes of the target product and file descriptive parameters indicative of file attributes of a CAD file corresponding to the target product.

15. The computer system according to claim 12, wherein, to establish the product process model, the processor is further configured to:

obtain data records associated with the product costs of the products and characteristic variables of the products;

select the plurality of characteristic parameters from the characteristic variables;

generate a computational model indicative of the interrelationships between product costs of one or more products and a respective plurality of characteristic parameters of the products;

determine desired statistical distributions of the plurality of characteristic parameters of the computational model; and

recalibrate the plurality of characteristic parameters based on the desired statistical distributions.

16. The computer system according to claim 15, wherein, to select the plurality of characteristic parameters, the processor is further configured to:

pre-process the data records; and

use a genetic algorithm to select the plurality of characteristic parameters from the characteristic variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

17. The computer system according to claim 15, wherein, to determine the desired statistical distributions, the processor is further configured to:

determine a candidate set of the characteristic parameters with a maximum zeta statistic using a genetic algorithm; and

determine the desired distributions of the characteristic parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.

18. A computer-readable medium for use on a computer system configured to perform a product cost predicting procedure, the computer-readable medium having computer-executable instructions for performing a method comprising:

establishing a product cost process model indicative of interrelationships between product costs of one or more existing products and a respective plurality of characteristic parameters of the existing products;

obtaining a set of values corresponding to a plurality of characteristic parameters of a target product from a data source;

calculating the product cost of the target product based upon the set of values corresponding to the plurality of characteristic parameters of the target product and the product cost process model; and

presenting the product cost of the target product to the data source.

19. The computer-readable medium according to claim 18, wherein the method further includes:

determining a desired product cost range of the target product; and

modifying the plurality of characteristic parameters of the target product simultaneously such that an actual product cost of the target product is within the desired product cost range.

20. The computer-readable medium according to claim 18, wherein the establishing includes:

obtaining data records associated with the product costs of the products and characteristic variables of the products;

selecting the plurality of characteristic parameters from the characteristic variables;

generating a computational model indicative of the interrelationships between product costs of one or more products and a respective plurality of characteristic parameters of the products;

determining desired statistical distributions of the plurality of characteristic parameters of the computational model; and

recalibrating the plurality of characteristic parameters based on the desired statistical distributions.

21. The computer-readable medium according to claim 20, wherein selecting further includes:

pre-processing the data records; and

using a genetic algorithm to select the plurality of characteristic parameters from the characteristic variables based on a mahalanobis distance between a normal data set and an abnormal data set of the data records.

22. The computer-readable medium according to claim 20, wherein generating further includes:

creating a neural network computational model;

training the neural network computational model using the data records; and

validating the neural network computation model using the data records.

23. The computer-readable medium according to claim 20, wherein determining further includes:

determining a candidate set of the characteristic parameters with a maximum zeta statistic using a genetic algorithm; and

determining the desired distributions of the characteristic parameters based on the candidate set,

wherein the zeta statistic ζ

is represented by;

$\mathrm{\zeta <br><br>}=\underset{1}{\overset{j}{\sum <br><br>}}<br><br>\underset{1}{\overset{i}{\sum <br><br>}}<br><br><br><br>S_{\mathrm{ij}}<br><br><br><br>\left(\frac{{\mathrm{\sigma <br><br>}}_i}{{\stackrel{\_}{x}}_i}\right)<br><br>\left(\frac{{\stackrel{\_}{x}}_j}{{\mathrm{\sigma <br><br>}}_j}\right),$

provided that x_i represents a mean of an ith input;

x_j represents a mean of a jth output;

σ

_i represents a standard deviation of the ith input;

σ

_j represents a standard deviation of the jth output; and

|S_ij| represents sensitivity of the jth output to the ith input of the computational model.