Optimized Deployment of Parts in a Supply Chain Network

• US 20080147490A1
• Filed: 02/15/2008
• Published: 06/19/2008
• Est. Priority Date: 10/26/2000
• Status: Active Grant

• Current Assignee: Blue Yonder Group, Inc. (Panasonic Holdings Corporation)
• Original Assignee: JDA Technologies US, Inc. (Panasonic Holdings Corporation)
• Inventors: Veerappanaicker, Dharmaraj, Kohli, Aseem, Najmi, Adeel
• Granted Patent: US 7,685,015 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• 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
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

Locations that include supply, manufacturing, demand locations, and channels are defined. A demand is computed for each part at each location. An availability lead-time is estimated for each part at each location and for each part at each channel. A total landed cost is calculated for each part at each location and each channel. A lead-time demand is computed for each part at each location using the availability lead-times for the part. A demand over lead-time is computed for each part at each location using the availability lead-times for the part. A completely filled demand is determined from the lead-time demands and the stock levels, and a partially filled demand is determined from the lead-time demands and the stock levels. A coverage function is generated for the parts at the locations and the channels from the completely filled demand and the partially filled demand.

Claims

1. A method for deploying a plurality of parts, comprising:

defining a plurality of locations comprising a plurality of supply locations, a plurality of manufacturing locations, a plurality of demand locations, and a plurality of channels;

computing a demand for each part at each location;

estimating an availability lead-time for each part at each location and for each part at each channel;

calculating a total landed cost for each part at each location and for each part at each channel;

computing a lead-time demand for each part at each location and for each part at each channel using the availability lead-times for the part;

computing a stock level for each part at each location and for each part at each channel;

determining a completely filled demand from the lead-time demands and the stock levels;

determining a partially filled demand from the lead-time demands and the stock levels; and

generating a coverage function for the parts at the locations and the channels from the completely filled demand and the partially filled demand.

2. The method of claim 1, further comprising:

transferring a demand from each of the plurality of demand locations and from each of the plurality of channels to one or more of the plurality of supply locations based on the order lead-time profiles;

creating virtual demand locations and virtual channels for the order lead-time demand transfers, wherein the virtual demand locations and the virtual channels represent the plurality of demand locations and the plurality of channels; and

generating a coverage function for the parts at the virtual locations and channels from the completely filled demand and the partially filled demand.

3. The method of claim 1, further comprising:

optimizing the coverage function; and

determining an optimal deployment of the parts at the locations for all the time periods according to the optimized coverage function.

4. The method of claim 1, further comprising:

determining a maximum stock level for the parts at the locations across the all the time periods in a multi-time period model; and

computing new purchases for each part at each location in a multi-time period model.

5. The method of claim 1, further comprising:

determining a replenishment policy for each part at each location and for each time period.

6. The method of claim 1, further comprising:

classifying the parts at each location into categories based on demand volume and cost;

generating a coverage function according to the part categories; and

determining a marginal value for minimizing the expected backorders and cost.

7. The method of claim 1, further comprising:

classifying intermediate locations;

grouping each intermediate location as echelons;

computing a total network cost according to the total landed cost of each part at each location;

using echelons for iterating coverage levels in order to find the optimal coverage levels for the intermediate locations according to the minimum total network cost; and

determining optimal coverage levels for the intermediate locations by iteratively minimizing the total network cost objective function.

8. The method of claim 1, further comprising:

creating one or more criticality groups of parts and service level bands based on one or more segmentation rules.

9. The method of claim 1, wherein calculating the total landed cost for each part at each location comprising a target demand location comprises:

computing the total landed cost for each part at a supply endpoint;

computing the total landed cost for each part at each supply location; and

computing the total landed cost for each part at each demand location according to the total landed cost for each part at the supply location.

10. The method of claim 1, wherein computing the lead-time demand for each part at each location comprising a manufacturing location comprises:

calculating a probability for supplying each part to each manufacturing location;

computing a process lead-time for an end product at each manufacturing location according to the probability; and

computing the lead-time demand of an end product at each manufacturing location.

11. The method of claim 1, wherein computing the lead-time demand for an end product at each manufacturing location comprises:

receiving an ordered list comprising at least a subset of each supply location;

calculating a probability for supplying a plurality of raw material parts to each manufacturing location; and

estimating the availability lead-time at each location based on the calculated probabilities.

12. The method of claim 1, wherein estimating the availability lead-time for an end product at each location comprising a target manufacturing location comprises:

determining the availability lead-time for an end product at each manufacturing location by repeating the following for a plurality of raw material parts;

estimating an availability lead-time for each raw material part at a supply endpoint;

estimating an availability lead-time for each raw material part at each supply location; and

estimating a process lead-time for the end product at each manufacturing location according to the availability lead-time for each raw material part at each supply location.

13. The method of claim 1, further comprising:

computing a demand for a plurality of raw material parts at each location comprising a supply location comprises;

calculating a demand for an end product at each manufacturing location operable to receive a plurality of raw material parts from each supply location;

calculating a dependent demand at each supply location according to the demand for the end product at each manufacturing location;

calculating an independent demand at each supply location; and

computing the demand at each supply location based on the dependent demand and the independent demand.

14. The method of claim 1, further comprising:

computing a demand for a plurality of raw material parts at each manufacturing location with an assembly process comprises;

calculating a demand for each raw material part from a demand for an end product at each manufacturing location based on a process yield;

calculating a demand for the end product at a demand endpoint;

calculating a demand for the end product at each demand location; and

calculating a demand for the end product at each supply location.

15. The method of claim 1, further comprising:

computing the demand for a plurality of raw material parts at each location comprising a manufacturing location comprises;

determining a total demand by calculating a demand for each raw material from a demand for each co-product at each manufacturing location based on a process yield;

calculating a demand for each co-product at a demand endpoint;

calculating a demand for each co-product at each demand location; and

calculating a demand for each co-product at each supply location.

16. A computer-implemented system for deploying a plurality of parts, comprising a server associated with one or more software components collectively operable to:

define a plurality of locations comprising a plurality of supply locations, a plurality of manufacturing locations, a plurality of demand locations, and a plurality of channels;

compute a demand for each part at each location;

estimate an availability lead-time for each part at each location and for each part at each channel;

calculate a total landed cost for each part at each location and for each part at each channel;

compute a lead-time demand for each part at each location and for each part at each channel using the availability lead-times for the part;

compute a stock level for each part at each location and for each part at each channel;

determine a completely filled demand from the lead-time demands and the stock levels;

determine a partially filled demand from the lead-time demands and the stock levels; and

generate a coverage function for the parts at the locations and the channels from the completely filled demand and the partially filled demand.

17. The system of claim 16, wherein the one or more software components are further operable to:

transfer a demand from each of the plurality of demand locations and from each of the plurality of channels to one or more of the plurality of supply locations based on the order lead-time profiles;

create virtual demand locations and virtual channels for the order lead-time demand transfers, wherein the virtual demand locations and the virtual channels represent the plurality of demand locations and the plurality of channels; and

generate a coverage function for the parts at the virtual locations and channels from the completely filled demand and the partially filled demand.

18. The system of claim 16, wherein the one or more software components are further operable to:

optimize the coverage function; and

determine an optimal deployment of the parts at the locations for all the time periods according to the optimized coverage function.

19. The system of claim 16, wherein the one or more software components are further operable to:

determine a maximum stock level for the parts at the locations across the all the time periods in a multi-time period model; and

compute new purchases for each part at each location in a multi-time period model.

20. The system of claim 16, wherein the one or more software components are further operable to:

determine a replenishment policy for each part at each location and for each time period.

21. The system of claim 16, wherein the one or more software components are further operable to:

classify the parts at each location into categories based on demand volume and cost;

generate a coverage function according to the part categories; and

determine a marginal value for minimizing the expected backorders and cost.

22. The system of claim 16, wherein the one or more software components are further operable to:

classify intermediate locations;

group each intermediate location as echelons;

compute a total network cost according to the total landed cost of each part at each location;

use echelons for iterating coverage levels in order to find the optimal coverage levels for the intermediate locations according to the minimum total network cost; and

determine optimal coverage levels for the intermediate locations by iteratively minimizing the total network cost objective function.

23. The system of claim 16, wherein the one or more software components are further operable to:

create one or more criticality groups of parts and service level bands based on one or more segmentation rules.

24. The system of claim 16, wherein the one or more software components are further operable to calculate the total landed cost for each part at each location comprising a target demand location by:

computing the total landed cost for each part at a supply endpoint;

computing the total landed cost for each part at each supply location; and

computing the total landed cost for each part at each demand location according to the total landed cost for each part at the supply location.

25. The system of claim 16, wherein the one or more software components are further operable to compute the lead-time demand for each part at each location comprising a manufacturing location by:

calculating a probability for supplying each part to each manufacturing location;

computing a process lead-time for an end product at each manufacturing location according to the probability; and

computing the lead-time demand of an end product at each manufacturing location.

26. The system of claim 16, wherein the one or more software components are further operable to compute the lead-time demand for an end product at each manufacturing location by:

receiving an ordered list comprising at least a subset of each supply location;

calculating a probability for supplying a plurality of raw material parts to each manufacturing location; and

estimating the availability lead-time at each location based on the calculated probabilities.

27. The system of claim 16, wherein the one or more software components are further operable to estimate the availability lead-time for an end product at each location comprising a target manufacturing location by:

determining the availability lead-time for an end product at each manufacturing location by repeating the following for a plurality of raw material parts;

estimating an availability lead-time for each raw material part at a supply endpoint;

estimating an availability lead-time for each raw material part at each supply location; and

estimating a process lead-time for the end product at each manufacturing location according to the availability lead-time for each raw material part at each supply location.

28. The system of claim 16, wherein the one or more software components are further operable to compute a demand for a plurality of raw material parts at each location comprising a supply location by:

calculating a demand for an end product at each manufacturing location operable to receive a plurality of raw material parts from each supply location;

calculating a dependent demand at each supply location according to the demand for the end product at each manufacturing location;

calculating an independent demand at each supply location; and

computing the demand at each supply location based on the dependent demand and the independent demand.

29. The system of claim 16, wherein the one or more software components are further operable to compute a demand for a plurality of raw material parts at each manufacturing location with an assembly process by:

calculating a demand for each raw material part from a demand for an end product at each manufacturing location based on a process yield;

calculating a demand for the end product at a demand endpoint;

calculating a demand for the end product at each demand location; and

calculating a demand for the end product at each supply location.

30. The system of claim 16, wherein the one or more software components are further operable to computing the demand for a plurality of raw material parts at each location comprising a manufacturing location by:

determining a total demand by calculating a demand for each raw material from a demand for each co-product at each manufacturing location based on a process yield;

calculating a demand for each co-product at a demand endpoint;

calculating a demand for each co-product at each demand location; and

calculating a demand for each co-product at each supply location.

31. Software for deploying a plurality of parts, the software embodied in a computer-readable medium and, when executed, operable to:

define a plurality of locations comprising a plurality of supply locations, a plurality of manufacturing locations, a plurality of demand locations, and a plurality of channels;

compute a demand for each part at each location;

estimate an availability lead-time for each part at each location and for each part at each channel;

calculate a total landed cost for each part at each location and for each part at each channel;

compute a lead-time demand for each part at each location and for each part at each channel using the availability lead-times for the part;

compute a stock level for each part at each location and for each part at each channel;

determine a completely filled demand from the lead-time demands and the stock levels;

determine a partially filled demand from the lead-time demands and the stock levels; and

generate a coverage function for the parts at the locations and the channels from the completely filled demand and the partially filled demand.

32. The software of claim 31, further operable to:

transfer a demand from each of the plurality of demand locations and from each of the plurality of channels to one or more of the plurality of supply locations based on the order lead-time profiles;

create virtual demand locations and virtual channels for the order lead-time demand transfers, wherein the virtual demand locations and the virtual channels represent the plurality of demand locations and the plurality of channels; and

generate a coverage function for the parts at the virtual locations and channels from the completely filled demand and the partially filled demand.

33. The software of claim 31, further operable to:

optimize the coverage function; and

determine an optimal deployment of the parts at the locations for all the time periods according to the optimized coverage function.

34. The software of claim 31, further operable to:

determine a maximum stock level for the parts at the locations across the all the time periods in a multi-time period model; and

compute new purchases for each part at each location in a multi-time period model.

35. The software of claim 31, further operable to:

determine a replenishment policy for each part at each location and for each time period.

36. The software of claim 31, further operable to:

classify the parts at each location into categories based on demand volume and cost;

generate a coverage function according to the part categories; and

determine a marginal value for minimizing the expected backorders and cost.

37. The software of claim 31, further operable to:

classify intermediate locations;

group each intermediate location as echelons;

compute a total network cost according to the total landed cost of each part at each location;

use echelons for iterating coverage levels in order to find the optimal coverage levels for the intermediate locations according to the minimum total network cost; and

determine optimal coverage levels for the intermediate locations by iteratively minimizing the total network cost objective function.

38. The software of claim 31, further operable to:

create one or more criticality groups of parts and service level bands based on one or more segmentation rules.

39. The software of claim 31, further operable to calculate the total landed cost for each part at each location comprising a target demand location by:

computing the total landed cost for each part at a supply endpoint;

computing the total landed cost for each part at each supply location; and

computing the total landed cost for each part at each demand location according to the total landed cost for each part at the supply location.

40. The software of claim 31, further operable to compute the lead-time demand for each part at each location comprising a manufacturing location by:

calculating a probability for supplying each part to each manufacturing location;

computing a process lead-time for an end product at each manufacturing location according to the probability; and

computing the lead-time demand of an end product at each manufacturing location.

41. The software of claim 31, further operable to compute the lead-time demand for an end product at each manufacturing location by:

receiving an ordered list comprising at least a subset of each supply location;

calculating a probability for supplying a plurality of raw material parts to each manufacturing location; and

estimating the availability lead-time at each location based on the calculated probabilities.

42. The software of claim 31, further operable to estimate the availability lead-time for an end product at each location comprising a target manufacturing location by:

determining the availability lead-time for an end product at each manufacturing location by repeating the following for a plurality of raw material parts;

estimating an availability lead-time for each raw material part at a supply endpoint;

estimating an availability lead-time for each raw material part at each supply location; and

estimating a process lead-time for the end product at each manufacturing location according to the availability lead-time for each raw material part at each supply location.

43. The software of claim 31, further operable to compute a demand for a plurality of raw material parts at each location comprising a supply location by:

calculating a demand for an end product at each manufacturing location operable to receive a plurality of raw material parts from each supply location;

calculating a dependent demand at each supply location according to the demand for the end product at each manufacturing location;

calculating an independent demand at each supply location; and

computing the demand at each supply location based on the dependent demand and the independent demand.

44. The software of claim 31, further operable to compute a demand for a plurality of raw material parts at each manufacturing location with an assembly process by:

calculating a demand for each raw material part from a demand for an end product at each manufacturing location based on a process yield;

calculating a demand for the end product at a demand endpoint;

calculating a demand for the end product at each demand location; and

calculating a demand for the end product at each supply location.

45. The software of claim 31, further operable to compute the demand for a plurality of raw material parts at each location comprising a manufacturing location by:

determining a total demand by calculating a demand for each raw material from a demand for each co-product at each manufacturing location based on a process yield;

calculating a demand for each co-product at a demand endpoint;

calculating a demand for each co-product at each demand location; and

calculating a demand for each co-product at each supply location.