Computation of supply chain planning process efficiency

• US 6,546,303 B1
• Filed: 02/05/2002
• Issued: 04/08/2003
• Est. Priority Date: 02/05/2002
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: May, Thomas A., Fried, David M.
• Primary Examiner(s): Picard, Leo
• Assistant Examiner(s): Masinick, Michael D.
• Application Number:
  US09/683,695
• Time in Patent Office: 427 Days
• Field of Search: 700/111, 700/106, 705/1, 705/8, 705/10-11, 705/28-29
• US Class Current: 700/106
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A method and system for computing a supply chain planning process efficiency E in relation to manufacturing activities of an organization. Input data, including supply and demand input data, is received beginning at time T₀. An iteration index R is set equal to 0. Then the method and system implements: incrementing R by 1; adjusting the input data (i.e., changing, deleting, or updating the input data); executing a central planning engine (CPE), using the input data; analyzing results of executing the CPE; and iterating, or computing and stopping. The iterating repeats the incrementing, adjusting, executing, and analyzing. The computing computes E. E may be a function of Q, C, and R^P/T. Q is a manual edit ratio in conjunction with adjusting the input data, C=S/D, S is supply, D is demand, T=T_E−T₀, and P is a real number.

Claims

1. A method for computing a supply chain planning process efficiency E, comprising the steps of:

receiving input data beginning at a time T₀, wherein the input data includes supply input data and demand input data;

setting an iteration index R equal to 0;

incrementing R by 1;

adjusting the input data;

executing a central planning engine, using the input data;

analyzing results of executing the central planning engine; and

deciding whether to iterate or to stop, and

if deciding to iterate then repeating the incrementing, adjusting, executing, analyzing, and deciding, or

if deciding to stop then computing followed by stopping, wherein the computing comprises computing at time T_E the efficiency E as a function of Q, C, and R^P/T, wherein Q is a manual edit ratio in conjunction with said adjusting the input data, wherein C=S/D, wherein S is supply, wherein D is demand, wherein T=T_E−

T₀, and wherein P is a real number.

2. The method of claim 1, wherein E is a decreasing function of Q, an increasing function of R^P/T, and a peaked function of C at C=1.

3. The method of claim 1, wherein P is selected from the group consisting of 0 and 1.

4. The method of claim 1, wherein deciding to stop occurs at R=1.

5. The method of claim 1, wherein S includes allocated supply and overage.

6. The method of claim 1, wherein D includes orders demand and forecast demand.

7. The method of claim 1, wherein D includes orders demand and does not include forecast demand.

8. The method of claim 1, wherein the deciding step is performed manually.

9. The method of claim 1, wherein the deciding step is performed by execution of software.

10. The method of claim 1, wherein E is an unstratified efficiency.

11. A system for computing a supply chain planning process efficiency (E), comprising a supply database, a demand database, and an algorithm adapted to:

receive input data beginning at a time T₀, wherein the input data includes supply input data from the supply database and demand input data from the demand database;

set an iteration index R equal to 0;

increment R by 1;

adjust the input data;

execute a central planning engine, using the input data;

analyze results of having executed the central planning engine; and

decide whether to iterate or to stop, and

if having decided to iterate then the algorithm adapted to again increment, adjust, execute, analyze, and decide, or

if having decided to stop then the algorithm adapted to compute and to stop, wherein to compute comprises to compute at time T_E the efficiency E as a function of Q, C, and R^P/T, wherein Q is a manual edit ratio in conjunction with said to adjust the input data, wherein C=S/D, wherein S is supply, wherein D is demand, wherein T=T_E−

T₀, and wherein P is a real number.

12. The system of claim 11, wherein E is a decreasing function of Q, an increasing function of R^P/T, and a peaked function of C at C=1.

13. The system of claim 11, wherein P is selected from the group consisting of 0 and 1.

14. The system of claim 11, wherein S includes allocated supply and overage.

15. The system of claim 11, wherein D includes orders demand and forecast demand.

16. The system of claim 11, wherein D includes orders demand and does not include forecast demand.

17. The system of claim 11, wherein to decide whether to iterate or to stop comprises to decide manually whether to iterate or to stop.

18. The system of claim 11, wherein to decide whether to iterate or to stop comprises to decide by execution of software whether to iterate or to stop.

19. The system of claim 11, wherein E is an unstratified efficiency.

20. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein for computing a supply chain planning process efficiency (E), said computer readable program code including an algorithm adapted to:

receive input data beginning at a time T₀, wherein the input data includes supply input data and demand input data;

set an iteration index R equal to 0;

increment R by 1;

adjust the input data;

execute a central planning engine, using the input data;

analyze results of having executed the central planning engine; and

decide whether to iterate or to stop, and

if having decided to iterate then the algorithm adapted to again increment, adjust, execute, analyze, and decide, or

if having decided to stop then the algorithm adapted to compute and to stop, wherein to compute comprises to compute at time T_E the efficiency E as a function of Q, C, and R^P/T, wherein Q is a manual edit ratio in conjunction with said to adjust the input data, wherein C=S/D, wherein S is supply, wherein D is demand, wherein T=T_E−

T₀, and wherein P is a real number.

23. The computer program product of claim 20, wherein E is a decreasing function of Q, an increasing function of R^P/T, and a peaked function of C at C=1.

24. The computer program product of claim 20, wherein P is selected from the group consisting of 0 and 1.

25. The computer program product of claim 20, wherein S includes allocated supply and overage.

26. The computer program product of claim 20, wherein D includes orders demand and forecast demand.

27. The computer program product of claim 20, wherein D includes orders demand and does not include forecast demand.

28. The computer program product of claim 20, wherein to decide whether to iterate or to stop comprises to decide manually whether to iterate or to stop.

29. The computer program product of claim 20, wherein to decide whether to iterate or to stop comprises to decide by execution of software whether to iterate or to stop.

30. The computer program product of claim 20, wherein E is an unstratified efficiency.

21. A method for computing a supply chain planning process efficiency E, comprising the steps of:

receiving input data, wherein the input data includes supply input data and demand input data;

setting an iteration index R equal to 0;

incrementing R by 1;

adjusting the input data;

executing a central planning engine, using the input data;

analyzing results of executing the central planning engine; and

deciding whether to iterate or to stop, and

if deciding to iterate then repeating the incrementing, adjusting, executing, analyzing, and deciding, or

if deciding to stop then computing followed by stopping, wherein the computing comprises computing the efficiency E.

22. A system for computing a supply chain planning process efficiency (E), comprising a supply database, a demand database, and an algorithm adapted to:

receive input data, wherein the input data includes supply input data from the supply database and demand input data from the demand database;

set an iteration index R equal to 0;

increment R by 1;

adjust the input data;

execute a central planning engine, using the input data;

analyze results of having executed the central planning engine; and

decide whether to iterate or to stop, and

if having decided to iterate then the algorithm adapted to again increment, adjust, execute, analyze, and decide, or

if having decided to stop then the algorithm adapted to compute and to stop, wherein to compute comprises to compute the efficiency E.