System and method for replenishment by purchase with attribute based planning

• US 6,885,902 B2
• Filed: 11/05/2002
• Issued: 04/26/2005
• Est. Priority Date: 12/27/2001
• Status: Expired due to Fees

• Current Assignee: JDA Software Group Incorporated (RedPrairie Holding Incorporated)
• Original Assignee: Manugistics Group Incorporated (RedPrairie Holding Incorporated)
• Inventors: Soman, Sunil, Crampton, Myrick
• Primary Examiner(s): Bahta, Kidest
• Application Number:
  US10/287,805
• Publication Number:
  US 20030126024A1
• Time in Patent Office: 903 Days
• Field of Search: 700 96- 97, 700 99-104, 700/32, 700/106, 700/108, 709/104, 709/102, 705/8
• US Class Current: 700/99
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/1093 Calendar-based scheduling for persons or groups
• CPC Class Codes: G06Q 20/203 Inventory monitoring

Abstract

A system and method for determining whether a supply chain network resource may be replenished by purchase in order to meet demand as defined by an order. The system and method is an attribute-based system and method that may be part of an overall system and method for planning the use of supply chain network resources. The system and method may generate a planning output that may indicate the feasibility of replenishment by purchase. The system and method recognizes time events and takes into account the exact sequence of the events in order to obtain an optimal solution.

Claims

1. A method for planning the replenishment of a supply chain network resource by purchase in order to fulfill demand as defined by an order, wherein said order defining a stock keeping unit with attribute, comprising the steps:

determining time items, wherein said time items are specific points in time or time periods relating to said order and wherein at least one of said items is a purchase lead time based on said stock keeping unit with attribute, and wherein said time items further comprising a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material;

determining timing events based on said time items;

creating a timeline comprising of said timing events and time items;

selecting an applicable scenario based on said timeline from a group of predetermined scenarios; and

generating an output based on said applicable scenario.

2. The method of claim 1, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

3. The method of claim 1, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

4. The method of claim 1, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

5. The method of claim 1, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

6. The method of claim 1, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

7. The method of claim 1, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

8. The method of claim 1, wherein said time events comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

9. The method of claim 8, wherein said scenarios are based on sequencing of said timing items and time events.

10. The method of claim 9, wherein said scenarios are each associated with specific outputs.

11. The method of claim 1, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

12. The method of claim 1, further comprising the step of creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.

13. A method for planning the replenishment of one or more supply chain network resources in order to fulfill demand as defined by orders, wherein said orders having attributes, comprising the steps:

prioritizing said orders according to said order attributes;

selecting highest priority order, wherein said highest priority order defining a stock keeping unit with attribute;

selecting one of said resources for fulfilling said order;

replenishing said resources in order to completely fulfill said order, wherein said replenishment step further comprising the steps;

determining time items, wherein said time items are specific points in time or time periods relating to said order and wherein at least one of said items is a purchase lead time based on said stock keeping unit with attribute;

determining timing events based on said time items;

creating a timeline comprising of said timing events and time items;

selecting an applicable scenario based on said timeline from a group of predetermined scenarios; and

generating an output based on said applicable scenario.

14. The method of claim 13, wherein said time items further comprising a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material.

15. The method of claim 14, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

16. The method of claim 14, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

17. The method of claim 14, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

18. The method of claim 14, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

19. The method of claim 14, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

20. The method of claim 14, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

21. The method of claim 14, wherein said time events comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

22. The method of claim 21, wherein said scenarios are based on sequencing of said timing items and time events.

23. The method of claim 22, wherein said scenarios are each associated with specific outputs.

24. The method of claim 13, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

25. The method of claim 13, further comprising the step of creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.

26. A system for planning replenishment of a supply chain network resource by purchasing material in order to fulfill demand as defined by an order, said order defining a stock keeping unit with attribute, comprising:

a computer device;

a database in communication with said device;

a module for determining time items, wherein said time items are specific points in time or time periods relating to said order and at least one of said time items is a purchase lead time based on said stock keeping unit with attribute, and wherein said time items comprise a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material;

a module for determining timing events based on said time items;

a module for creating a timeline comprising of said timing events and time items;

a module for selecting an applicable scenario based on said timeline from a group of predetermined scenarios;

and a module for generating an output based on said applicable scenario.

27. The system of claim 26, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

28. The system of claim 26, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

29. The system of claim 26, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

30. The system of claim 26, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

31. The system of claim 26, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

32. The system of claim 26, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

33. The system of claim 26, wherein said time events further comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

34. The system of claim 33, wherein said scenarios are based on sequencing of said timing items and time events.

35. The system of claim 34, wherein said scenarios are each associated with specific outputs.

36. The system of claim 26, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

37. The system of claim 26, further comprising a module for creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.

38. A program storage device readable by a machine, tangibly embodying a program of instructions executable by a machine to perform method steps of planning for the replenishment of supply chain network resources in order to fulfill demand as defined by one or more orders, wherein said one or more orders having attributes and each said orders defining a stock keeping unit with attribute, comprising the steps:

prioritizing said orders according to said order attributes;

selecting highest priority order;

selecting one of said resources for fulfilling said order;

replenishing said resources in order to completely fulfill said order, wherein said replenishment step further comprising the steps;

determining time items, wherein said time items are specific points in time or time periods relating to said order and wherein at least one of said time items is a purchase lead time based on said stock keeping unit with attribute;

determining timing events based on said time items;

creating a timeline comprising of said timing events and time items;

selecting an applicable scenario based on said timeline from a group of predetermined scenarios; and

generating an output based on said applicable scenario.

39. The program storage device of claim 38, wherein said time items further comprising a purchase order placement date, a replenish start date, a replenish need date, a maximum lateness, and a maximum earliness, wherein said purchase lead time is defined as amount of time needed to make available requested replenish material.

40. The program storage device of claim 39, wherein said purchase order placement date is defined as the difference between said replenish need date and said purchase lead time.

41. The program storage device of claim 39, wherein said time events comprising of a date as defined by the sum of purchase order placement date and maximum lateness.

42. The program storage device of claim 39, wherein said time events comprising of a date as defined by the difference between said replenish need date minus maximum earliness.

43. The program storage device of claim 39, wherein said time events comprising of a date as defined by the sum of said replenish need date and said maximum lateness.

44. The program storage device of claim 39, wherein said time events comprising of a date as defined by the sum of said replenish start date and purchase lag time.

45. The program storage device of claim 39, wherein said time events comprising of a date as defined by the difference between purchase order placement date minus maximum earliness.

46. The program storage device of claim 39, wherein said time events comprising of dates as defined by the sum of purchase order placement date and maximum lateness, the difference between said replenish need date minus maximum earliness, the sum of said replenish need date and said maximum lateness, the sum of said replenish start date and purchase lag time, and the difference between purchase order placement date minus maximum earliness.

47. The program storage device of claim 46, wherein said scenarios are based on sequencing of said timing items and time events.

48. The program storage device of claim 47, wherein said scenarios are each associated with specific outputs.

49. The program storage device of claim 38, wherein said outputs generated will be full replenishment is possible and on time, full replenishment is not possible on time, or replenishment is infeasible.

50. The program storage device of claim 38, further comprising the step of creating a purchase model, wherein said purchase model is modeled by defining period maximums to restrict the amount of materials that a resource may produce over a time period.