CARBON MANAGEMENT FOR SOURCING AND LOGISTICS

• US 20100131316A1
• Filed: 11/26/2008
• Published: 05/27/2010
• Est. Priority Date: 11/26/2008
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Boucher, Chad, Katircloglu, Kaan K., Ben-Hamida, Mondher, Sourirajan, Karthik, Centonze, Paolina, Helander, Mary E.
• Granted Patent: US 8,346,595 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: Y02P 90/84 Greenhouse gas [GHG] management systems
• CPC Class Codes: Y02P 90/845 Inventory and reporting systems for greenhouse gases [GHG]
• CPC Class Codes: Y02P 90/90 Financial instruments for climate change mitigation, e.g. environmental taxes, subsidies or financing

Abstract

Embodiments of the invention provide a method, system and computer program product for carbon management for sourcing and logistics. In one embodiment, the method comprises using a computer for quantifying both a cost and a carbon impact of one or more logistics policies relating to a manufacturing process; and minimizing the cost and carbon impact using a defined equation including a first component representing a transportation cost, and a second component representing a carbon cost. In an embodiment of the invention, the quantifying includes using an analytics engine to quantify the cost and carbon impact. The analytics engine may include a shipment analysis module to calculate an optimal transportation policy, a sourcing analysis module for testing alternate sourcing options, a scenario analysis module to find an optimal order frequency, and a sensitivity analysis module to test the impact of various changes.

Claims

1. A method of carbon management for sourcing and logistics, comprising:

using a computer for;

quantifying a cost, a service and a carbon impact of one or more logistics policies relating to a manufacturing process; and

minimizing said cost and carbon impact using a defined equation including a first component representing a transportation cost, a second component representing an inventory cost and a third component representing a carbon cost.

2. The method according to claim 1, wherein the quantifying includes using an analytics engine to quantify said cost and carbon impact, said analytics engine including a shipment analysis module to calculate an optimal transportation policy by minimizing logistics costs.

3. The method according to claim 2, wherein the analytics engine further includes a sourcing analysis module for testing alternate sourcing options.

4. The method according to claim 2, wherein the analytics engine further includes a scenario analysis module to find an optimal order frequency for each part that minimizes the total cost subject to a service constraint and decides on the best transportation mode for a given order consolidation policy.

5. The method according to claim 4, wherein the said consolidation policy is selected from the group comprising:

using planned order frequencies for shipments without any additional consolidation effort;

finding the optimal order frequency and safety lead time for every individual shipment;

consolidating all shipments that are sourced from the same supplier and finding the optimal order frequency and safety lead time for the consolidated set of shipments;

consolidating shipments that are sourced within a defined geographical area bound for a destination that is defined within a determined geographical area;

consolidating shipments across suppliers within the same state by executing a milk-run across these suppliers and finding the optimal order frequency and safety lead time for the consolidated set of shipments;

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of shipments; and

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant, moving suppliers closer to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of shipments.

6. The method according to claim 2, wherein the analytics engine further includes a sensitivity analysis module to test the impact of changes to uncontrollable or uncertain input parameters.

7. The method according to claim 1, wherein the quantifying includes using a regression model for transportation cost calculation by mode of transportation.

8. The method according to claim 1, wherein the quantifying includes calculating an origin to destination air distance using latitude and longitudinal data.

9. The method according to claim 8, wherein the quantifying includes calculating an origin to distance road distance by adjusting the origin to destination to air distance by a road distance correction factor.

10. The method according to claim 1, wherein:

said first component is the transportation cost and is modeled as the sum of a fixed cost component, which depends on the fixed cost per shipment and the order frequency (a.n), and a variable cost component that depends on the variable cost per mile-ton transported and the total mile tons transported (b.w.m.D);

said second component is the inventory carrying charge, the average inventory level at the plant is the sum of the work-in-process inventory and the safety stock inventor;

the work-in-process inventory, given by

11. A system for carbon management for sourcing and logistics, the system comprising one or more processor units configured for:

quantifying both a cost and a carbon impact of one or more logistics policies relating to a manufacturing process; and

minimizing said cost, and carbon impact using a defined equation including a first component representing a transportation cost, a second component representing an inventory cost and a third component representing a carbon cost.

12. The system according to claim 11, wherein the quantifying is done by using an analytics engine to quantify said cost and carbon impact, said analytics engine including:

a shipment analysis module to calculate an optimal transportation policy by minimizing logistics costs;

a sourcing analysis module for testing alternate sourcing options;

a scenario analysis module to find an optimal order frequency for each part that minimizes the total cost subject to a service constraint and decides on the best transportation mode for a given order consolidation policy; and

sensitivity analysis module to test the impact of changes to uncontrollable or uncertain input parameters.

13. The system according to claim 12, wherein the said consolidation policy is selected from the group comprising:

using planned order frequencies for shipments without any additional consolidation effort;

finding the optimal order frequency and safety lead time for every individual shipment;

consolidating all shipments that are sourced from the same supplier and finding the optimal order frequency and safety lead time for the consolidated set of shipments;

consolidating shipments that are sourced within a defined geographical area bound for a destination that is defined within a determined geographical area;

consolidating shipments across suppliers within the same state by executing a milk-run across these suppliers and finding the optimal order frequency and safety lead time for the consolidated set of shipments;

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of shipments; and

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant, moving suppliers closer to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of shipments.

14. The system according to claim 11, wherein the quantifying includes:

calculating an origin to destination air distance using latitude and longitudinal data; and

calculating an origin to destination ro ad distance by adjusting the origin to destination air distance by a road distance correction factor.

15. The system according to claim 11, wherein:

said first component is the transportation cost is modeled as the sum of a fixed cost component, which depends on the fixed cost per shipment and the order frequency (a.n), and a variable cost component that depends on the variable cost per mile-ton transported and the total mile tons transported (b.w.m.D);

said second component is the inventory carrying charge, the average inventory level at the plant is the sum of the work-in-process inventory and the safety stock inventory. The work-in-process inventory, given by

16. An article of manufacture comprising:

at least one computer usable medium having computer readable program code logic to execute a machine instruction in a processing unit for carbon management for sourcing and logistics, said computer readable program code logic, when executing, performing the following steps;

quantifying both a cost and a carbon impact of one or more logistics policies relating to a manufacturing process; and

minimizing said cost and carbon impact using a defined equation including a first component representing a transportation cost, a second component representing an inventory cost and a third component representing a carbon cost.

17. The article of manufacture according to claim 16, wherein the quantifying is done by using an analytics engine to quantify said cost and carbon impact, said analytics engine including:

a shipment analysis module to calculate an optimal transportation policy by minimizing logistics costs;

a sourcing analysis module for testing alternate sourcing options;

a scenario analysis module to find an optimal order frequency for each part that minimizes the total cost subject to a service constraint and decides on the best transportation mode for a given order consolidation policy; and

sensitivity analysis module to test the impact of changes to uncontrollable or uncertain input parameters.

18. The article of manufacture according claim 17, wherein the said consolidation policy is selected from the group comprising:

using planned order frequencies for shipments without any additional consolidation effort;

finding the optimal order frequency and safety lead time for every individual shipment;

consolidating all shipments that are sourced from the same supplier and finding the optimal order frequency and safety lead time for the consolidated set of shipments;

consolidating shipments that are sourced within a defined geographical area bound for a destination that is defined within a determined geographical area;

consolidating shipments across suppliers within the same state by executing a milk-run across these suppliers and finding the optimal order frequency and safety lead time for the consolidated set of shipments;

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of shipments; and

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant, moving suppliers closer to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of shipments.

19. The article of manufacture according to claim 16, wherein the quantifying includes:

calculating an origin to destination air distance using latitude and longitudinal data; and

calculating an origin to destination road distance by adjusting the origin to destination air distance by a road distance correction factor.

20. The article of manufacture according to claim 16, wherein:

said first component is the transportation cost is modeled as the sum of a fixed cost component, which depends on the fixed cost per shipment and the order frequency (a.n), and a variable cost component that depends on the variable cost per mile-ton transported and the total mile tons transported (b.w.m.D);

said second component is the inventory carrying charge, the average inventory level at the plant is the sum of the work-in-process inventory and the safety stock inventory, the work-in-process inventory, given by

21. A method of carbon management for sourcing and logistics, comprising:

using a computer for;

quantifying a cost and a carbon impact of a plurality of logistics policies relating to a manufacturing process by using an analytics engine to quantify said cost and carbon impact; and

minimizing said cost and carbon impact using a defined equation including a first component representing a transportation cost, a second component representing an inventory cost, and a third component representing a carbon cost;

wherein the analytics engine includes a shipment analysis module to calculate an optimal transportation policy by minimizing logistic costs, and a sourcing analysis module for testing alternate sourcing options.

22. The method according to claim 21, wherein the analytics engine further includes a scenario analysis module to find an optimal order frequency fore each part of a multitude of parts that minimizes the total cost subject to a service constraint, and decides on a best transportation mode for a given order consolidation policy, said order consolidation policy being selected from the group comprising:

using planned order frequencies for parts without any additional consolidation effort;

finding the optimal order frequency and safety lead time for every individual part;

consolidating all parts that are sourced from the same supplier and finding the optimal order frequency and safety lead time for the consolidated set of parts;

consolidating parts across suppliers within the same state by executing a milk-run across these suppliers and finding the optimal order frequency and safety lead time for the consolidated set of parts;

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of parts; and

reducing the supplier base by assuming that all suppliers in a given state can be consolidated to the location in the state closest to the manufacturing plant, moving suppliers closer to the manufacturing plant and finding the optimal order frequency and safety lead time for the consolidated set of parts.

23. The method according to claim 21, wherein the analytics engine further includes a sensitivity analysis module to test the impact of changes to uncontrollable or uncertain input parameters.

24. The method according to claim 23, wherein the quantifying includes using a regression model for transportation cost calculation by mode of transportation.

25. The method according to claim 21, wherein:

said first component is modeled as the sum of a fixed cost component, which depends on the fixed cost per shipment and the order frequency (a.n), and a variable cost component that depends on the variable cost per mile-ton transported and the total mile tons transported (b.w.m.D);

said second component is the inventory carrying charge, wherein the average inventory level at the plant is the sum of the work-in-process inventory and the safety stock inventory;

the work-in-process inventory, given by