IP Library Granted Patent US 10,748,096
Granted Patent B2
US 10,748,096 · App. 15/865,575 · Granted Aug 18, 2020

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: Blue Yonder Group, Inc.
G06Q10/06315G06Q10/06G06Q10/063G06Q10/06375G06Q10/087G06Q30/0202
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Quick Facts
Patent No.
US 10,748,096
App. No.
15/865,575
Granted
Aug 18, 2020
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 (95)

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 demand parameter of one of the one or more entities;

determining a cost associated with the demand parameter and a benefit associated with 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 associated with the demand parameter exceeds the cost associated with the demand parameter;

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

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.

3. The computer-implemented method of claim 1 , 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.

4. The computer-implemented method of claim 1 , wherein the demand parameter comprises a mean demand or the demand variability.

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

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

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

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

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

6. The computer-implemented method of claim 5 , 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 5 , 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.

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

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 at one of the one or more entities;

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

determine a cost associated with the demand parameter and a benefit associated with 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 associated with the demand parameter exceeds the cost associated with the demand parameter;

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

11. The system of claim 9 , 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.

12. The system of claim 9 , further comprising:

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

determine a cost associated with the supply parameter and a benefit associated with 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 associated with the supply parameter exceeds the cost associated with the supply parameter; and

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

13. The system of claim 12 , 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.

14. The system of claim 12 , 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 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 demand parameter of one of the one or more entities;

determine a cost associated with the demand parameter and a benefit associated with 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 associated with the demand parameter exceeds the cost associated with the demand parameter;

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

17. The non-transitory computer-readable medium software of claim 15 , 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.

18. The non-transitory computer-readable medium software of claim 15 , wherein the one or more computers further performs the following steps: change a supply parameter of one of the one or more entities; determine a cost associated with the supply parameter and a benefit associated with 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 associated with the supply parameter exceeds the cost associated with the supply parameter; and adjust the inventory target of one of the one or more entities based on the adjusted supply parameter.

19. The non-transitory computer-readable medium software of claim 18 , 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.

20. The non-transitory computer-readable medium software of claim 18 , 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 (7)
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 14, 2020
From: JDA SOFTWARE GROUP, INC.
To: BLUE YONDER GROUP, INC.
Reel/Frame 052393/0538 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 11, 2018
From: DOGAN, KORAY; NAJMI, ADEEL; SHEIKHZADEH, MEDHI; RAMAN, RAMESH
To: I2 TECHNOLOGIES US, INC.
Reel/Frame 045784/0127 →
CHANGE OF NAME Recorded May 11, 2018
From: I2 TECHNOLOGIES US, INC.
To: JDA TECHNOLOGIES US, INC.
Reel/Frame 046137/0521 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 11, 2018
From: JDA TECHNOLOGIES US, INC.
To: JDA SOFTWARE GROUP, INC.
Reel/Frame 045784/0144 →
Continuity (6)
Continuation 14331038 · Jul 14, 2014
Continuation 13902893 · May 27, 2013
Continuation 13163687 · Jun 18, 2011
Continuation 10836448 · Apr 29, 2004
Provisional Application 60470068 · May 12, 2003
Related Publication 20180130000A1 · May 10, 2018