Back to results
US 8,527,322 B2Granted
Proactive demand shaping for a configurable product portfolio with uncertain demand
AssigneeInternational Business Machines Corporation
InventorsMarkus R. Ettl, Anshul Sheopuri, Karthik Sourirajan
Life of this patent
Estimating the term…
Abstract
Proactive demand shaping of configurable products with uncertain demand computes a lower bound and an upper bound on optimal profit by considering future demand distribution of one or more products, supply outlook of one or more components and customer behavior. The upper bound and the lower bound solutions are used to generate one or more rationing heuristics. Simulation may be used to determine a rationing policy to implement in practice.
Claims15
A method for proactive demand shaping of configurable products with uncertain demand, comprising:
- computing, using a processor, a lower bound and an upper bound on profit by considering future demand distribution of one or more products, supply outlook of one or more components and customer behavior;
- and
- generating one or more rationing heuristics based on one or more solutions of the lower bound and the upper bound,
- wherein computing the lower bound comprises solving a non-linear programming objective function that characterizes allocating the one or more components before demand is realized and that formulates expected profit from demand shaping and computing the upper bound comprises solving a linear programming objective function that characterizes allocating the one or more components to demand after the demand is realized and that formulates expected profit from demand shaping,
- wherein the non-linear programming objective function maximizes expected profit expressed as revenue from selling products subtracted by penalties due to back orders and excess components, subject to constraints of restriction due to available supply of components, demand allocation requirement, restriction on substitutability of products and non-negativity requirements.
2 more elements
A method for proactive demand shaping of configurable products with uncertain demand, comprising:
- computing, using a processor, a lower bound and an upper bound on profit by considering future demand distribution of one or more products, supply outlook of one or more components and customer behavior;
- and
- generating one or more rationing heuristics based on one or more solutions of the lower bound and the upper bound, wherein computing a lower bound includes solving an objective function,
- Z
- l
- *
- =
- max
- x
- ,
- π
-
- Z
- =
- E
-
- [
- ∑
- j
- =
- 1
- P
-
- r
- j
-
- ∑
- k
- =
- 1
- P
-
- min
-
- {
- x
- jk
- ,
- π
- jk
- ·
- D
- k
- }
- -
- ∑
- k
- =
- 1
- P
-
- b
- k
-
- ∑
- j
- =
- 1
- P
-
- (
- π
- jk
- ·
- D
- k
- -
- x
- jk
- )
- +
- -
- ∑
- i
- =
- 1
- M
-
- h
- i
- ·
- (
- y
- i
- -
- ∑
- j
- =
- i
- P
-
- ∑
- k
- =
- 1
- P
-
- v
- ij
-
- x
- jk
- )
- -
- ∑
- j
- =
- 1
- P
-
- H
- j
-
- ∑
- k
- =
- 1
- P
-
- (
- π
- jk
- ·
- D
- k
- -
- x
- jk
- )
- -
- ]
- subject
-
-
- to
- ∑
- j
- =
- i
- P
-
- ∑
- k
- =
- 1
- P
-
- v
- ij
-
- x
- jk
- ≤
- y
- i
- ,
-
- ∀
- i
- ∑
- j
- =
- 1
- P
-
- π
- jk
- =
- 1
- ,
-
- ∀
- k
- π
- jk
- =
- x
- jk
- =
- 0
- ,
-
- ∀
- k
- ∉
- A
- j
- π
- jk
- ,
- x
- jk
- ≥
- 0
- ,
-
- ∀
- j
- ,
- k
- wherein the objective function models an expected profit of a system in which the one or more components are allocated before demand is realized, and
- ∑
- j
- =
- 1
- P
-
- r
- j
-
- ∑
- k
- =
- 1
- P
-
- min
-
- {
- x
- jk
- ,
- π
- jk
- ·
- D
- k
- }
- represents revenue from selling products,
- ∑
- k
- =
- 1
- P
-
- b
- k
-
- ∑
- j
- =
- 1
- P
-
- (
- π
- jk
- ·
- D
- k
- -
- x
- jk
- )
- +
- represents penalty due to backorders, and
- -
- ∑
- i
- =
- 1
- M
-
- h
- i
- ·
- (
- y
- i
- -
- ∑
- j
- =
- i
- P
-
- ∑
- k
- =
- 1
- P
-
- v
- ij
-
- x
- jk
- )
- -
- ∑
- j
- =
- 1
- P
-
- H
- j
-
- ∑
- k
- =
- 1
- P
-
- (
- π
- jk
- ·
- D
- k
- -
- x
- jk
- )
- -
-
- ]
- represent penalties due to excess components,
- ∑
- j
- =
- i
- P
-
- ∑
- k
- =
- 1
- P
-
- v
- ij
-
- x
- jk
- ≤
- y
- i
- ,
- ∀i represents constraints of restrictions due to available supply of components,
- ∑
- j
- =
- 1
- P
-
- π
- jk
- =
- 1
- ,
-
- ∀
- k
- represents constraints of demand allocation requirement,
- πjk=xjk=0, ∀kεAj represents constraints of restrictions on substitutability of products, and
- πjk,xjk≧0, ∀j, k represents constraints of non-negativity requirements.
354 more elements
A method for proactive demand shaping of configurable products with uncertain demand, comprising:
- computing, using a processor, a lower bound and an upper bound on profit by considering future demand distribution of one or more products, supply outlook of one or more components and customer behavior;
- and
- generating one or more rationing heuristics based on one or more solutions of the lower bound and the upper bound, wherein computing an upper bound includes solving an objective function,
- Z
- u
- *
- =
- max
- w
-
- ∑
- n
- =
- 1
- N
-
- ϕ
-
- (
- n
- )
- [
- ∑
- j
- =
- 1
- P
-
- r
- j
- ·
- ∑
- k
- =
- 1
- P
-
- w
- jk
- (
- n
- )
- -
- ∑
- k
- =
- 1
- P
-
- b
- k
- ·
- (
- d
- k
- (
- n
- )
- -
- ∑
- j
- =
- 1
- P
-
- w
- jk
- (
- n
- )
- )
- -
- ∑
- i
- =
- 1
- M
-
- h
- i
- ·
- (
- y
- i
- -
- ∑
- j
- =
- 1
- P
-
- v
- ij
-
- ∑
- k
- =
- 1
- P
-
- w
- jk
- (
- n
- )
- )
- ]
-
- subject
-
-
- to
-
- ∑
- j
- =
- 1
- P
-
- w
- jk
- (
- n
- )
- ≤
- w
- k
- (
- n
- )
- ,
-
- ∀
- k
- ,
- n
-
- ∑
- j
- =
- 1
- P
-
- v
- ij
-
- ∑
- k
- =
- 1
- P
-
- ·
- w
- jk
- (
- n
- )
- ≤
- y
- i
- ,
-
- ∀
- n
- ,
- i
-
- w
- jk
- (
- n
- )
- =
- 0
- ,
-
- ∀
- n
- ,
- k
- ∉
- A
- j
-
- w
- jk
- (
- n
- )
- ≥
- 0
- ,
-
- ∀
- n
- ,
- j
- ,
- k
- wherein the objective function maximizes profit, and
- ∑
- n
- =
- 1
- N
-
- ϕ
-
- (
- n
- )
-
- (
- ∑
- j
- =
- 1
- P
-
- r
- j
- ·
- ∑
- k
- =
- 1
- P
-
- w
- jk
- (
- n
- )
- )
- represents revenue generated by selling products,
- ∑
- n
- =
- 1
- N
-
- ϕ
-
- (
- n
- )
- [
- ∑
- k
- =
- 1
- P
-
- b
- k
- ·
- (
- d
- k
- (
- n
- )
- -
- ∑
- j
- =
- 1
- P
-
- w
- jk
- (
- n
- )
- )
- ]
- represents backorder penalties for not satisfying demand excess component,
- ∑
- n
- =
- 1
- N
-
- ϕ
-
- (
- n
- )
- [
- ∑
- i
- =
- 1
- M
-
- h
- i
- ·
- (
- y
- i
- -
- ∑
- j
- =
- 1
- P
-
- v
- ij
-
- ∑
- k
- =
- 1
- P
-
- ·
- w
- jk
- (
- n
- )
- )
- ]
- represents inventory holding costs, and
- ∑
- j
- =
- 1
- P
-
- w
- jk
- (
- n
- )
- ≤
- w
- k
- (
- n
- )
- ,
- ∀k,n represents constraints of demand allocation,
- ∑
- j
- =
- 1
- P
-
- v
- ij
-
- ∑
- k
- =
- 1
- P
-
- ·
- w
- jk
- (
- n
- )
- ≤
- y
- i
- ,
- ∀n,i represents constraints of supply restrictions,
- wjk(n)=0, ∀n, kεAj represents constraints of restrictions on substitutions, and
- wjk(n)≧0, ∀n, j, k represents constraints of non-negativity constraints.
371 more elements
A system for proactive demand shaping of configurable products with uncertain demand, comprising:
- a processor;
- and
- a computing module operable to execute on the processor, and further operable to compute a lower bound and an upper bound on a profit by considering future demand distribution of products, supply outlook of components and customer behavior, the computing module further operable to generate one or more rationing heuristics based on one or more solutions of the lower bound and the upper bound,
- wherein the lower bound is computed by solving a non-linear programming objective function that characterizes allocating the one or more components before demand is realized and that formulates expected profit from demand shaping and the upper bound is computed by solving a linear programming objective function that characterizes allocating the one or more components to demand after the demand is realized and that formulates expected profit from demand shaping, wherein the non-linear programming objective function maximizes expected profit expressed as revenue from selling products subtracted by penalties due to back orders and excess components, subject to constraints of restriction due to available supply of components, demand allocation requirement, restriction on substitutability of products and non-negativity requirements.
1 more element
A system for proactive demand shaping of configurable products with uncertain demand, comprising:
- a processor;
- and
- a computing module operable to execute on the processor, and further operable to compute a lower bound and an upper bound on a profit by considering future demand distribution of products, supply outlook of components and customer behavior, the computing module further operable to generate one or more rationing heuristics based on one or more solutions of the lower bound and the upper bound,
- wherein the lower bound is computed by solving a non-linear programming objective function that characterizes allocating the one or more components before demand is realized and that formulates expected profit from demand shaping and the upper bound is computed by solving a linear programming objective function that characterizes allocating the one or more components to demand after the demand is realized and that formulates expected profit from demand shaping, wherein the linear programming objective function maximizes profit expressed as revenue generated by selling products subtracted by penalties for not satisfying demand and excess component, and inventor holding costs, subject to constraints of demand allocation, supply restriction, restriction on substitution and restriction on non-negativity.
1 more element
A computer readable storage medium storing a program of instructions executable by a machine to perform a method of proactive demand shaping of configurable products with uncertain demand, comprising:
- computing, using a processor, a lower bound and an upper bound on optimal profit by considering future demand distribution of one or more products, supply outlook of one or more components and customer behavior;
- and
- generating one or more rationing heuristics based on one or more solutions of the lower bound and the upper bound,
- wherein computing the lower bound comprises solving a non-linear programming objective function that characterizes allocating the one or more components before demand is realized and that formulates expected profit from demand shaping and computing the upper bound comprises solving a linear programming objective function that characterizes allocating the one or more components to demand after the demand is realized and that formulates expected profit from demand shaping,
- wherein the linear programming objective function maximizes profit expressed as revenue generated by selling products subtracted by penalties for not satisfying demand and excess component, and inventor holding costs, subject to constraints of demand allocation, supply restriction, restriction on substitution and restriction on non-negativity.
2 more elements
Dependencies are inferred from the claim text.