IP Library Granted Patent US 9,870,544
Granted Patent B2
US 9,870,544 · App. 14/331,038 · Granted Jan 16, 2018

Determining an inventory target for a node of a supply chain

Inventors: Koray Dogan (Boston, MA); Adeel Najmi (Plano, TX); Mehdi Sheikhzadeh (Irving, TX); Ramesh Raman (San Carlos, CA)
Assignee: JDA Software Group, Inc.
G06Q10/06315G06Q10/06G06Q10/063G06Q10/06375G06Q10/087G06Q30/0202
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Quick Facts
Patent No.
US 9,870,544
App. No.
14/331,038
Granted
Jan 16, 2018
Kind
B2
Abstract

Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias.

Claims (99)

1. A computer-implemented method for distributing items to one or more locations in a supply chain network, comprising:

calculating a demand stock based on satisfying a demand over supply lead time at one or more locations of one or more entities in the supply chain network, the one or more entities comprising one or more computers;

calculating a demand variability stock based on satisfying a demand variability of the demand over supply lead time at one of the one or more entities;

establishing a demand bias of the demand at one of the one or more entities;

determining an inventory target of one of the one or more entities on the demand stock and the demand variability stock based, at least in part on the demand bias;

changing a supply parameter of one of the one or more entities;

determining a cost of the supply parameter and a benefit of the supply parameter based on the change in the supply parameter;

automatically adjusting, by a processor, the supply parameter of one of the one or more entities, when the benefit of the supply parameter exceeds the cost of the supply parameter;

automatically adjusting, by the processor, the inventory target of one of the one or more entities based on the adjusted supply parameter;

communicating, by a computer network, the adjusted inventory target to one or more locations of the one or more entities; and

distributing one or more items to one or more locations of the one or more entities based on the adjusted inventory target.

2. The computer-implemented method of claim 1 , further comprising:

changing a demand parameter of one of the one or more entities;

determining a cost of the demand parameter and a benefit of the demand parameter based on the change in the demand parameter;

adjusting the demand parameter of one of the one or more entities, when the benefit of the demand parameter exceeds the cost of the demand parameter; and

adjusting the inventory target of one of the one or more entities based on the adjusted demand parameter.

3. The computer-implemented method of claim 1 , wherein the supply parameter comprises the supply lead time or supply lead time variability.

4. The computer-implemented method of claim 2 , wherein the demand parameter comprises a mean demand or the demand variability.

5. The computer-implemented method of claim 1 , further comprising:

determining that a predicted demand is less than an actual demand; and

using the demand stock but not the demand variability stock in determining the inventory target.

6. The computer-implemented method of claim 2 , further comprising:

changing a second demand parameter of one of the one or more entities;

determining a second demand cost based on the change in the second demand parameter;

changing a second supply parameter of one of the one or more entities;

determining a second supply benefit based on the change in the second supply parameter; and

determining the inventory target of one of the one or more entities based on the change in the supply parameter, the change in the demand parameter, and the comparison of the second demand cost and the second supply benefit.

7. The computer-implemented method of claim 2 , further comprising:

generating a demand forecast;

determining the demand over supply lead time from the demand forecast; and

determining the demand variability of the demand over supply lead time from the demand forecast.

8. The computer-implemented method of claim 2 , further comprising:

identifying a business model associated with the supply chain; and

adjusting the supply parameter and the demand parameter in accordance with the business model.

9. A system for distributing items to one or more locations in a supply chain network, comprising:

an optimization engine tangibly embodied on a non-transitory computer-readable medium, and one or more computers operating in a networking environment and configured to perform the following steps by the one or more computers:

calculate a demand stock based on satisfying a demand over supply lead time at one or more locations of one or more entities in the supply chain network, the one or more entities comprising one or more computers;

calculate a demand variability stock based on satisfying a demand variability of the demand over supply lead time one of the one or more entities;

establish a demand bias of the demand one of the one or more entities;

determine an inventory target of one of the one or more entities based on the demand stock and the demand variability stock in accordance with the demand bias;

change a supply parameter of one of the one or more entities;

determine a cost of the supply parameter and a benefit of the supply parameter based on the change in the supply parameter;

adjust the supply parameter of one of the one or more entities, when the benefit of the supply parameter exceeds the cost of the supply parameter;

adjust the inventory target of one of the one or more entities based on the adjusted supply parameter;

communicate the adjusted inventory target to one or more locations of the one or more entities; and

distribute one or more items to one or more locations of the one or more entities based on the adjusted inventory target.

10. The system of claim 9 , further comprising:

change a demand parameter of one of the one or more entities;

determine a cost of the demand parameter and a benefit of the demand parameter based on the change in the demand parameter;

adjust the demand parameter of one of the one or more entities, when the benefit of the demand parameter exceeds the cost of the demand parameter; and

adjust the inventory target of one of the one or more entities based on the adjusted demand parameter.

11. The system of claim 9 , wherein the optimization engine is further configured to calculate the inventory target of the entity, by:

determining that a predicted demand is less than an actual demand; and

using the demand stock but not the demand variability stock in determining the inventory target.

12. The system of claim 10 , wherein the computer system is further configured to:

change a second demand parameter of one of the one or more entities;

determine a second demand cost based on the change in the second demand parameter;

change a second supply parameter of one of the one or more entities;

determine a second supply benefit based on the change in the second supply parameter; and

determine the inventory target of one of the one or more entities based on the change in the supply parameter, the change in the demand parameter, and the comparison of the second demand cost and the second supply benefit.

13. The system of claim 10 , wherein the computer system is further configured to:

generate a demand forecast;

determine the demand over supply lead time from the demand forecast; and

determine the demand variability of the demand over supply lead time from the demand forecast.

14. The system of claim 10 , wherein the computer system is further configured to:

identify a business model associated with the supply chain; and

adjust the supply parameter and the demand parameter in accordance with the business model.

15. A non-transitory computer-readable medium embodied with software for distributing items to one or more locations in a supply chain network, the software when executed by one or more computers, performs the following steps:

calculate a demand stock based on satisfying a demand over supply lead time at one or more locations of one or more entities in the supply chain network, the one or more entities comprising one or more computers;

calculate a demand variability stock based on satisfying a demand variability of the demand over supply lead time at one of the one or more entities;

establish a demand bias of the demand at one of the one or more entities;

determine the inventory target of the entity one of the one or more entities based on the demand stock and the demand variability stock in accordance with the demand bias;

change a supply parameter of one of the one or more entities determine a cost of the supply parameter and a benefit of the supply parameter based on the change in the supply parameter;

adjust the supply parameter of one of the one or more entities, when the benefit of the supply parameter exceeds the cost of the supply parameter;

adjust the inventory target of one of the one or more entities based on the adjusted supply parameter;

communicate the adjusted inventory target to one or more locations of the one or more entities; and

distribute one or more items to one or more locations of the one or more entities based on the adjusted inventory target.

16. The non-transitory computer-readable medium software of claim 15 , wherein the one or more computers further performs the following steps:

change a demand parameter of one of the one or more entities;

determine a cost of the demand parameter and a benefit of the demand parameter based on the change in the demand parameter;

adjust the demand parameter of one of the one or more entities, when the benefit of the

demand parameter exceeds the cost of the demand parameter; and

adjust the inventory target of one of the one or more entities based on the adjusted demand parameter.

17. The non-transitory computer-readable medium software of claim 15 , wherein the one or more computers further calculates the inventory target of the entity, by:

determining that a predicted demand is less than an actual demand; and

using the demand stock but not the demand variability stock in determining the inventory target.

18. The non-transitory computer-readable medium software of claim 16 , wherein the one or more computers further performs the following steps:

change a second demand parameter of one of the one or more entities;

determine a second demand cost based on the change in the second demand parameter;

change a second supply parameter of one of the one or more entities;

determine a second supply benefit based on the change in the second supply parameter; and

determine the inventory target of one of the one or more entities based on the change in the supply parameter, the change in the demand parameter, and the comparison of the second demand cost and the second supply benefit.

19. The non-transitory computer-readable medium software of claim 16 , wherein the one or more computers performs the following steps:

generate a demand forecast;

determine the demand over supply lead time from the demand forecast; and

determine the demand variability of the demand over supply lead time from the demand forecast.

20. The non-transitory computer-readable medium software of claim 16 , wherein the one or more computers performs the following steps:

identify a business model associated with the supply chain; and

adjust the supply parameter and the demand parameter in accordance with the business model.

Assignments (8)
RELEASE OF SECURITY INTEREST IN PATENTS PREVIOUSLY RECORDED AT REEL/FRAME (053383/0117) Recorded Nov 3, 2021
From: U.S. BANK NATIONAL ASSOCIATION, AS COLLATERAL AGENT
To: BLUE YONDER GROUP, INC.
Reel/Frame 058794/0776 →
RELEASE OF SECURITY INTEREST Recorded Sep 16, 2021
From: JPMORGAN CHASE BANK, N.A.
To: BLUE YONDER GROUP, INC.; BLUE YONDER, INC.; JDA SOFTWARE SERVICES, INC.; I2 TECHNOLOGIES INTERNATIONAL SERVICES, LLC; MANUGISTICS SERVICES, INC.; MANUGISTICS HOLDINGS DELAWARE II, INC.; REDPRAIRIE COLLABORATIVE FLOWCASTING GROUP, LLC; JDA SOFTWARE RUSSIA HOLDINGS, INC.; REDPRAIRIE SERVICES CORPORATION; BY BOND FINANCE, INC.; BY NETHERLANDS HOLDING, INC.; BY BENELUX HOLDING, INC.
Reel/Frame 057724/0593 →
SECURITY AGREEMENT Recorded Aug 3, 2020
From: BLUE YONDER GROUP, INC.
To: U.S. BANK NATIONAL ASSOCIATION
Reel/Frame 053383/0117 →
CHANGE OF NAME Recorded Apr 13, 2020
From: JDA SOFTWARE GROUP, INC.
To: BLUE YONDER GROUP, INC.
Reel/Frame 052386/0114 →
SECURITY AGREEMENT Recorded Oct 12, 2016
From: RP CROWN PARENT, LLC; RP CROWN HOLDING LLC; JDA SOFTWARE GROUP, INC.
To: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT
Reel/Frame 040326/0449 →
CHANGE OF NAME Recorded Jul 16, 2014
From: I2 TECHNOLOGIES US, INC.
To: JDA TECHNOLOGIES US, INC.
Reel/Frame 033342/0125 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 16, 2014
From: JDA TECHNOLOGIES US, INC.
To: JDA SOFTWARE GROUP, INC.
Reel/Frame 033324/0554 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 16, 2014
From: DOGAN, KORAY; NAJMI, ADEEL; SHEIKHZADEH, MEHDI; RAMAN, RAMESH
To: I2 TECHNOLOGIES US, INC.
Reel/Frame 033324/0015 →
Continuity (5)
Continuation 13902893 · May 27, 2013
Continuation 13163687 · Jun 18, 2011
Continuation 10836448 · Apr 29, 2004
Provisional Application 60470068 · May 12, 2003
Related Publication 20150134397A1 · May 14, 2015