Method and system for product optimization

• US 7,219,068 B2
• Filed: 01/11/2002
• Issued: 05/15/2007
• Est. Priority Date: 03/13/2001
• Status: Active Grant

• Current Assignee: Ford Motor Company
• Original Assignee: Ford Motor Company
• Inventors: King, John, Zelek, Carolyn, Brockers, Toni, Vora, Mahesh Himatlal, Soderborg, Nathan R.
• Primary Examiner(s): Jeanty; Romain
• Application Number:
  US10/043,696
• Publication Number:
  US 20030040954A1
• Time in Patent Office: 1,950 Days
• Field of Search: 705/10, 705/7
• US Class Current: 705/7.29
• 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 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A method and computer-implemented system for optimizing a product. Based on the “voice of the customer”, aspects of the product that are critical to the customer satisfaction, and target values, therefore, are identified. Aspects are characterized in terms of their contributing factors. Each contributing factor is characterized in a transfer function in terms of control and noise factors impacting the contributing factors. Contributing factors are optimized during product design by shifting nominal design values for control factors with respect to the transfer function such that target contributing factors are attained with minimum variability due to existing noises and variability in control. Where target values cannot be obtained through design optimization, conventional methods of manufacturing optimization are implemented. The extent to which the target values are attained and maintained over the life of the product are assessed.

Claims

1. A method for product optimization, the method comprising:

identifying at least one aspect of a product that is critical to customer satisfaction and a target value for at least one aspect;

characterizing the at least one aspect in terms of at least one contributing factor y, and characterizing the at least one contributing factor y in terms of at least one control factor x wherein the at least one contributing factor y is additionally characterized in terms of at least one contributing noise factor n and a transfer function y=f(x,n);

receiving input defining a nominal value and a variability value for the at least one noise factor n;

outputting a nominal and a variability value the at least one contributing factor y based on the transfer function, a nominal design value and variability value for the at least one control factor x, and the nominal value and the variability value for the at least one noise factor n; and

adjusting nominal design values for the at least one control factor x such that variability in the at least one contributing factor y is minimized and the target value for the at least one aspect is attained thereby optimizing the product.

2. The method of claim 1 wherein the at least one aspect and target value for the at least one aspect are identified based on consumer insight.

3. The method of claim 1 further comprising identifying a target value for the at least one aspect in an aged condition;

and

minimizing variability in the at least one contributing factor such that the target value for the at least one aspect in the aged condition is attained.

4. The method of claim 1 additionally comprising minimizing variability in the at least one contributing factor by reducing variability in the at least one control factor.

5. The method of claim 1 wherein the variability in the at least one contributing factor is reduced to a 6σ

level.

6. The method of claim 1 further comprising assessing the extent to which the target value is attained over the product life.

7. A computer-implemented system for facilitating product optimization, the system configured to:

receive input defining a transfer function characterizing a contributing factor y to a product aspect that is critical to customer satisfaction in terms of at least one control factor x for the contributing factor y;

receive input defining a nominal design value and a variability value for the at least one control factor x; and

output a mean and variability value for the contributing factor y based on the transfer function and the nominal and variability values for the at least one control factor x wherein a transfer function y=f(x,n) characterizes the contributing factor y in terms of the at least one control factor x and at least one noise factor n, the system additionally configured to receive input defining a nominal value and a variability value for each at least one noise factor n and output a nominal and a variability value for the contributing factor y based on the transfer function, the nominal design value and variability value for the at least one control factor x, and the nominal value and the variability value for the at least one noise factor n thereby facilitating product optimization.

8. The system of claim 7 wherein the variability in the contributing factor is reduced in response to input adjusting the nominal design value(s) for the at least one control factor.

9. The system of claim 7 wherein the variability in the contributing factor is reduced in response to input reducing the variability value(s) for the at least one control factor.

10. The system of claim 8 or 9 wherein the variability of the contributing factor is reduced to a 6σ

level.

11. A method for product optimization, the method comprising:

a step for identifying at least one aspect of a product that is critical to customer satisfaction and a target value for the at least one aspect;

a step for characterizing the at least one aspect in terms of at least one contributing factor y;

a step for characterizing the at least one contributing factor y in terms of at least one control factor x and at least one noise factor n wherein the step for characterizing the at least one contributing factor y involves a step for defining a transfer function y=f(x,n);

a step for receiving input defining a nominal value and a variability value for the at least one noise factor n;

a step for outputting a nominal and a variability value for the at least one contributing factor y based on the transfer function, a nominal design value and variability value for the at least one control factor x, and the nominal value and the variability value for the at least one noise factor n; and

a step for minimizing variability in the at least one contributing factor y such that the target value for the aspect is attained thereby optimizing the product.

12. The method of claim 11 wherein the step for minimizing variability in the at least one contributing factor involves adjusting a nominal design value for the at least one control factor.

13. The method of claim 11 wherein the step for minimizing variability in the at least one contributing factor involves reducing variability in the at least one control factor.

14. The method of claim 11 additionally comprising a step for assessing the extent to which the target value for the aspect is attained over the product life.

15. A computer-implemented system for facilitating product optimization, the system comprising:

a means for receiving a transfer function y=f(x,n) characterizing a contributing factor y to a product aspect that is critical to customer satisfaction in terms of at least one control factor x and at least one noise factor n;

a means for receiving a nominal design value and a variability value for the at least one control factor x;

a means for defining a nominal value and a variability value for the at least one noise factor n; and

a means for computing a mean and variability value for the contributing factor y based on the transfer function, the nominal design and variability values for the at least one control factor x, and the nominal and variability value for the at least one noise factor n thereby facilitating product optimization.