Predictive cost reduction based on a thermodynamic model

• US 8,069,122 B2
• Filed: 03/20/2008
• Issued: 11/29/2011
• Est. Priority Date: 03/20/2007
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: George, Michael L.
• Primary Examiner(s): BORISSOV, IGOR N
• Application Number:
  US12/052,533
• Publication Number:
  US 20080235067A1
• Time in Patent Office: 1,349 Days
• Field of Search: 705/400, 705/7, 705/8, 705/9, 705/10, 705/11, 705/29, 705/30, 700 28- 35, 700/36, 700 44- 46, 700/67, 703/2, 702/182
• US Class Current: 705/400
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

Predictive cost reduction based on a thermodynamic model, in which parameters associated with a process are accessed. The parameters include a quantity of units of work-in-process at first and second times, and first and second constants respectively indicative of growth between the first and second times, and of a translated reduction of the work-in-process to a reduction of cost. A thermodynamic model is applied to the accessed parameters, and a predictive cost reduction associated with an improvement of the process based on applying the thermodynamic model is output.

Claims

1. A cost reduction system comprising:

a computer-readable storage medium storing a thermodynamic model that is configured to determine a predictive cost reduction for a process, the predictive cost reduction being derived from thermodynamic principles;

a processor configured to;

access parameters associated with a process, the parameters comprising;

a quantity of units of work-in-process at first and second times, and

first and second constants respectively indicative of growth between the first and second times, and of a reduction of the work-in-process to a reduction of cost,

apply the thermodynamic model to the accessed parameters, and

determine a predictive cost reduction associated with a contemplated improvement of the process, using;

2. A computer-implemented method:

accessing parameters associated with a process, the parameters comprising;

a quantity of units of work-in-process at first and second times, and

first and second constants respectively indicative of growth between the first and second times, and of a reduction of the work-in-process to a reduction of cost;

applying, by one or more computers, a thermodynamic model to the accessed parameters, wherein applying the thermodynamic model further comprises determining the predictive cost reduction using;

3. The computer-implemented method of claim 2, wherein the thermodynamic model is derived from Carnot'"'"'s equation.

4. The computer-implemented method of claim 3, wherein the thermodynamic model derived from Carnot'"'"'s equation comprises an expression analogous to Carnot'"'"'s equation, the expression being derived from Little'"'"'s Law.

5. The computer-implemented method of claim 3, wherein the cost reduction is proportional to the logarithm of a reduction in the quantity of units of work-in-process from the first time to the second time.

6. The computer-implemented method of claim 2, wherein the second constant indicative of a reduction in work-in-process to a reduction in cost comprises a constant indicative of a reduction in entropy corresponding to a reduction in cost.

7. The computer-implemented method of claim 2, wherein the first constant indicative of growth comprises a ratio of an economic value at the second time and the economic value at the first time.

8. The computer-implemented method of claim 7, wherein the economic value at the second time represents demand at the second time, and the economic value at the first time represents demand at the first time.

9. The computer-implemented method of claim 7, wherein the economic value at the second time represents revenue at the second time, and the economic value at the first time represents revenue at the first time.

10. The computer-implemented method of claim 7, wherein the economic value at the second time represents units produced at the second time, and the economic value at the first time represents units produced at the first time.

11. The computer-implemented method of claim 2, wherein the second constant has a value between 0.09 and 0.11.

12. The computer-implemented method of claim 2, further comprising determining the first and second constants based on empirical data.

13. The computer-implemented method of claim 2, wherein the process comprises value-added costs and non-value added costs, and the non-value added costs comprise 50% or more of a total cost associated with the process.

14. The computer-implemented method of claim 13, wherein the non-value added costs comprise rework of at least one of the units of work-in-process, the rework including performing the at least one of the units of work in process more than one time.

15. The computer-implemented method of claim 14, wherein the total cost associated with the process is driven by the rework.

16. The computer implemented method of claim 2, further comprising modifying the process based on the predictive cost reduction.

17. A computer program product, tangibly embodied in a non-transitory machine-readable medium, the computer program product comprising instructions that, when read by a machine, operate to cause a data processing apparatus to:

access parameters associated with a process, the parameters comprising;

a quantity of units of work-in-process at first and second times, and

first and second constants respectively indicative of growth between the first and second times, and of a reduction of the work-in-process to a reduction of cost;

apply a thermodynamic model to the accessed parameter by determining the predictive cost reduction using;

18. The computer program product of claim 17, wherein the thermodynamic model is derived from Carnot'"'"'s equation.