Generalizing an optimized supplier allocation plan

• US 9,875,447 B2
• Filed: 02/04/2011
• Issued: 01/23/2018
• Est. Priority Date: 03/01/2002
• Status: Active Grant

• Current Assignee: Blue Yonder Group, Inc. (Panasonic Holdings Corporation)
• Original Assignee: JDA Software Group Incorporated (Panasonic Holdings Corporation)
• Inventors: Ganesan, Vijay Krishnam, Yang, Jian, Hayes, Gayle Lynee, Miller, Mark Richard
• Primary Examiner(s): Iwarere, Oluseye
• Application Number:
  US13/021,280
• Publication Number:
  US 20110125470A1
• Time in Patent Office: 2,545 Days
• Field of Search: 705 28, 705 736, 705 722
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

Generating an optimized supplier allocation plan includes identifying parts and suppliers associated with an allocation problem, where each supplier can supply at least one part. One or more objective functions are selected. Each objective function has part variables, and each part variable represents a quantity of a part to be procured from a supplier. At least one constraint constraining at least one part variable is received. The one or more objective functions are optimized with respect to the at least one constraint to yield a value for each part variable. A quantity of each part to be procured from at least one supplier is determined according to the values to generate the optimized supplier allocation plan.

Claims

1. A system, comprising:

a supply chain network comprising one or more suppliers and two or more sites;

a computer system coupled with a database stored on a non-transitory computer readable medium, the computer system comprising a processor, a memory, an object model configured to generate a mathematical model representing an allocation problem as an optimization problem comprising an objective function and one or more constraints, wherein each of the one or more objective functions comprises two or more part variables, each part variable representing a quantity of a part to be procured from the one or more suppliers, the computer system configured to;

receive the one or more constraints from the database;

optimize the objective function subject to the one or more constraints to solve the mathematical model and determine an optimized solution to the allocation problem;

receive one or more inputs comprising modifications to at least one of the objective function and the one or more constraints from the database;

modify the one or more constraints based on the one or more inputs;

determine a correlation between the one or more constraints and the resulting optimized solution by solving a new mathematical model based on the one or more modified constraints;

modify the object model based on the one or more inputs;

determine a correlation between the modified object model and the resulting optimized solution by solving a second new mathematical model based on the modified object model; and

determine a quantity of each part to be procured from at least one of the one or more suppliers based on the optimized solution of the allocation problem; and

cause the quantity of each part to be procured by at least one of the two or more sites based, at least in part, on the optimized solution of the allocation problem.

2. The system of claim 1, wherein the one or more constraints are selected from the set comprising business rules, contract requirements, and supplier factors.

3. The system of claim 1, wherein the one or more constraints are business rules comprising a requirement to meet a projected demand for each of two or more parts at each of the two or more sites for a particular time period.

4. The system of claim 1, wherein the one or more constraints are business rules comprising a requirement that the number of suppliers for one or more parts fall within a specified range.

5. The system of claim 1, wherein the one or more constraints are contract rules comprising rebates, discounts, and penalties.

6. The system of claim 1, wherein the one or more constraints are contract rules, and the objective function is optimized to maximize contract compliance with respect to the contract rules.

7. The system of system of claim 1, wherein the one or more constraints are a set of supplier factors comprising supplier evaluation performance scores.

8. A computer-implemented method, comprising:

initiating an object model configured to generate a mathematical model representing an allocation problem as an optimization problem comprising an objective function and one or more constraints, wherein each of the one or more objective functions comprises two or more part variables, each part variable representing a quantity of a part to be procured from one or more suppliers;

receiving the one or more constraints from a database stored on a non-transitory computer- readable medium coupled with a computer system;

optimizing the objective function subject to the one or more constraints to solve the mathematical model and determine an optimized solution to the allocation problem;

receiving one or more inputs comprising modifications to at least one of the objective function and the one or more constraints from the database;

modifying the one or more constraints based on the one or more inputs;

determining a correlation between the modified one or more constraints and the resulting optimized solution by solving a new mathematical model based on the one or more modified constraints;

modifying the object model based on the one or more inputs;

determining a correlation between the modified object model and the resulting optimized solution by solving a second new mathematical model based on the modified object model;

determining, by at least one supplier, a quantity of each part to be procured based on the optimized solution of the allocation problem; and

causing the quantity of each part to be procured by at least one of the two or more sites based, at least in part, on the optimized solution of the allocation problem.

9. The computer-implemented method of claim 8, wherein the one or more constraints are selected from the set of business rules, contract requirements, and supplier factors.

10. The computer-implemented method of claim 8, wherein the one or more constraints are business rules comprising a requirement to meet a projected demand for each of two or more parts at each of the two or more sites for a particular time period.

11. The computer-implemented method of claim 8, wherein the one or more constraints are business rules comprising a requirement that the number of suppliers for one or more parts fall within a specified range.

12. The computer-implemented method of claim 8, wherein the one or more constraints are contract rules comprising rebates, discounts, and penalties.

13. The computer-implemented method of claim 8, wherein the one or more constraints are contract rules, and the objective function is optimized to maximize contract compliance with respect to the contract rules.

14. The computer-implemented method of claim 8, wherein the one or more constraints are a set of supplier factors comprising supplier evaluation performance scores.

15. A non-transitory computer-readable medium embodied with software, the software when executed using one or more computers is configured to:

initiate an object model to generate a mathematical model representing an allocation problem as an optimization problem comprising an objective function and one or more constraints wherein each of the one or more objective functions comprises two or more part variables, each part variable representing a quantity of a part to be procured from one or more suppliers;

receive the one or more constraints from a database;

optimize the objective function subject to the one or more constraints to solve the mathematical model and determine an optimized solution to the allocation problem;

receive one or more inputs comprising modifications to at least one of the objective function and the one or more constraints from the database;

modify the one or more constraints based on the one or more inputs;

determine a correlation between the modified one or more constraints and the resulting optimized solution by solving a new mathematical model based on the one or more modified constraints;

modify the object model based on the one or more inputs;

determine a correlation between the modified object model and the resulting optimized solution by solving a second new mathematical model based on the modified object model;

determine a quantity of each part to be procured from at least one supplier based on the optimized solution of the allocation problem; and

cause the quantity of each part to be procured by at least one of the two or more sites based, at least in part, on the optimized solution of the allocation problem.

16. The non-transitory computer-readable medium of claim 15, wherein the one or more constraints are selected from the set of business rules, contract requirements, and supplier factors.

17. The non-transitory computer-readable medium of claim 15, wherein the one or more constraints are business rules comprising a requirement to meet a projected demand for each of two or more parts at each of the two or more sites for a particular time period.

18. The non-transitory computer-readable medium of claim 15, wherein the one or more constraints are business comprising a requirement that the number of suppliers for one or more parts fall within a specified range.

19. The non-transitory computer-readable medium of claim 15, wherein the one or more constraints are contract rules comprising rebates, discounts, and penalties.

20. The non-transitory computer-readable medium of claim 15, wherein the one or more constraints are contract rules, and the objective function is optimized to maximize contract compliance with respect to the contract rules.