US2013117061A1PendingUtilityA1
Carbon management for sourcing and logistics
Est. expiryNov 26, 2028(~2.3 yrs left)· nominal 20-yr term from priority
Inventors:Mondhar Ben-HamidaChad BoucherPaolina CentonzeMary E. HelanderKaan K. KatirclogluKarthik Sourirajan
G06Q 10/06315G06Q 10/08G06Q 30/0283Y02P90/845Y02P90/84Y02P90/90
57
PatentIndex Score
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Claims
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
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method of sourcing and logistics for a manufacturing process, said sourcing and logistics based on carbon management, and wherein in the manufacturing process specified product parts are shipped from supplies to an assembly plant, comprising:
quantifying a cost, a service and a carbon impact of one or more logistics policies relating to the manufacturing process; determining a minimum of said cost and carbon impact using a defined optimization equation to determine an optimal combination of a multitude of components relating to the shipment of the product parts from the suppliers to the assembly plant, said multitude of components including a first component representing a transportation cost for shipping the product parts from the suppliers to the assembly plant, a second component representing an inventory cost and a third component representing a carbon cost for carbon emissions from inbound transportation activities of the shipping the product parts from the suppliers to the assembly plant; and using a computer system, executing a sourcing and logistics program, to implement the quantifying a cost, a service and a carbon impact, and the minimizing said cost and carbon impact.
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 compute carbon emissions from inbound transportation activities of shipping the product parts from the suppliers to the assembly plant.
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 product part that minimizes the total cost subject to a service constraint and decides on a 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; 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 route 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 . A method according to claim 1 , wherein:
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); the inventory cost includes an inventory carrying charge, and an average inventory level at the plant is the sum of a work-in-process inventory and a safety stock inventory; the work-in-process inventory, given by
(
D
2
n
)
,
depends on demand and order frequency; the safety stock inventory captures the additional inventory in the plant due to the expected amount of time by which each part is early; the safety stock, given by (E(s−T) + D), depends on demand, safety lead time, delivery delay distribution; the inventory carrying charge is modeled as the product of the average inventory level and the unit carrying charge (hC);
the carbon cost is defined as the product of the carbon price per mile-ton transported and the total mile-tons transported (ψ.w.m.D); the carbon price per mile-ton transported is obtained as the product of the carbon emission factor and the carbon price per ton of emission; and
said service constraint is the manufacturing availability target constraint given by:
n
[
E
(
1
n
+
s
-
T
)
+
-
E
(
s
-
T
)
+
]
≥
α
where
D=Constant demand rate of a given part,
T=Random variable representing supply delay,
α=Target inventory availability to manufacturing,
h=Annual inventory carrying rate (in %),
C=Unit cost (in $),
w=Unit weight (in tons),
m=Distance of supplier from manufacturing plant (in miles),
a=Fixed transportation cost parameter (in $ per shipment),
b=Variable transportation cost parameter (in $ per mile-ton),
ψ=Carbon cost (in $ per mile-ton),
s=Safety lead time associated with the orders to help meet target inventory availability,
n=Stationary order frequency given by
(
D
Q
)
,
E(.)=Expected value function; and
(x−y) + =Maximum of (x−y) and zero.
11 . A system for sourcing and logistics for a manufacturing process, said sourcing and logistics based on carbon management, and wherein in the manufacturing process specified product parts are shipped from supplies to an assembly plant, 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 the manufacturing process; and determining a minimum said cost and carbon impact using a defined optimization equation to determine an optimal combination of a multitude of components relating to the shipment of the product parts from the suppliers to the assembly plant, said multitude of components including a first component representing a transportation cost for shipping the product parts from the suppliers to the assembly plant, a second component representing an inventory cost and a third component representing a carbon cost for carbon emissions from inbound transportation activities of the shipping the product parts from the suppliers to the assembly plant.
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 compute carbon emissions from inbound transportation activities of shipping the product parts from the suppliers to the assembly plant; 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 a 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; 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 route 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 road distance by adjusting the origin to destination air distance by a road distance correction factor.
15 . The system according to claim 11 , wherein:
said 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 inventory cost includes an inventory carrying charge, and an average inventory level at the plant is the sum of a work-in-process inventory and a safety stock inventory; the work-in-process inventory, given by
(
D
2
n
)
,
depends on demand and order frequency;
the safety stock inventory captures the additional inventory in the plant due to the expected amount of time by which each part is early, this safety stock, given by (E(s−T) + D), depends on demand, safety lead time, delivery delay distribution;
the inventory carrying charge is modeled as the product of the average inventory level and the unit carrying charge (hC);
said carbon cost is defined as the product of the carbon price per mile-ton transported and the total mile-tons transported (ψ.w.m.D);
the carbon price per mile-ton transported is obtained as the product of the carbon emission factor and the carbon price per ton of emission; and
said service constraint is the manufacturing availability target constraint given by:
n
[
E
(
1
n
+
s
-
T
)
+
-
E
(
s
-
T
)
+
]
≥
α
where
D=Constant demand rate of a given part,
T=Random variable representing supply delay,
α=Target inventory availability to manufacturing,
h=Annual inventory carrying rate (in %),
C=Unit cost (in $),
w=Unit weight (in tons),
m=Distance of supplier from manufacturing plant (in miles),
a=Fixed transportation cost parameter (in $ per shipment),
b=Variable transportation cost parameter (in $ per mile-ton),
ψ=Carbon cost (in $ per mile-ton),
s=Safety lead time associated with the orders to help meet target inventory availability,
n=Stationary order frequency given by
(
D
Q
)
,
E(.)=Expected value function, and
(x−y) + =Maximum of (x−y) and zero.
16 . An article of manufacture comprising:
at least one tangible computer usable device having computer readable program code logic tangibly embodied therein to execute a machine instruction in a processing unit for sourcing and logistics for a manufacturing process, said sourcing and logistics based on carbon management, and wherein in the manufacturing process specified product parts are shipped from supplies to an assembly plant, 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 the manufacturing process; and determining a minimum of said cost and carbon impact using a defined optimization equation to determine an optimal combination of a multitude of components relating to the shipment of the product parts from the suppliers to the assembly plant, said multitude of components including a first component representing a transportation cost for shipping product parts from suppliers to an assembly plant, a second component representing an inventory cost and a third component representing a carbon cost for carbon emissions from inbound transportation activities of the shipping the product parts from the suppliers to the assembly plant.
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 compute carbon emissions from inbound transportation activities of shipping the product parts from the suppliers to the assembly plant; 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 a 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 consolidation policy is selected from the group comprising:
using planned order frequencies for shipments 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 route 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 longitude data; and calculating an origin to destination road distance by adjusting the origin to destination are distance by a road distance correction factor.
20 . The article of manufacture according to claim 16 , wherein:
said 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 inventory cost includes an inventory carrying charge, and an average inventory level at the plant is the sum of a work-in-process inventory and a safety stock inventory, the work-in-process inventory, given by
(
D
2
n
)
,
depends on demand and order frequency;
the safety stock inventory captures the additional inventory in the plant due to the expected amount of time by which each part is early, this safety stock, given by (E(s−T) + D), depends on demand, safety lead time, delivery delay distribution;
the inventory carrying charge is modeled as the product of the average inventory level and the unit carrying charge (hC);
said third component is a carbon cost, defined as the product of the carbon price per mile-ton transported and the total mile-tons transported (ψ.w.m.D);
the carbon price per mile-ton transported is obtained as the product of the carbon emission factor and the carbon price per ton of emission; and
said service constraint is the manufacturing availability target constraint given by:
n
[
E
(
1
n
+
s
-
T
)
+
-
E
(
s
-
T
)
+
]
≥
α
where
D=Constant demand rate of a given part,
T=Random variable representing supply delay,
α=Target inventory availability to manufacturing,
h=Annual inventory carrying rate (in %),
C=Unit cost (in $),
w=Unit weight (in tons),
m=Distance of supplier from manufacturing plant (in miles),
a=Fixed transportation cost parameter (in $ per shipment),
b=Variable transportation cost parameter (in $ per mile-ton),
ψ=Carbon cost (in $ per mile-ton),
s=Safety lead time associated with the orders to help meet target inventory availability,
n=Stationary order frequency given by
(
D
Q
)
,
E(.)=Expected value function, and
(x−y) + =Maximum of (x−y) and zero.Join the waitlist — get patent alerts
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