System and method for simultaneous price optimization and asset allocation to maximize manufacturing profits

• US 7,921,061 B2
• Filed: 09/05/2007
• Issued: 04/05/2011
• Est. Priority Date: 09/05/2007
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Thapliyal, Saurabh, Santhanam, Sharad, Rangarajan, Keshava
• Primary Examiner(s): BORISSOV, IGOR N
• Application Number:
  US11/850,558
• Publication Number:
  US 20090063251A1
• Time in Patent Office: 1,308 Days
• Field of Search: 705/400, 705/7, 705/10, 705/28, 705/29, 705/30, 705/32, 703/2, 703/6, 703/23
• US Class Current: 705/400
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

Systems and methods in accordance with various embodiments of the present invention provide for a system and method for simultaneous price optimization and asset allocation to maximize manufacturing profits. In one embodiment, a set of price points for the item and a set of expected demand values for each price point are determined. A supply-side constraint which models inventory, replenishment, and capacities associated with replenishment and a joining constraint, which requires that the set of expected demand values be equal to a planned supply of the item, are determined. A demand-side constraint is determined. Further, an objective function to maximize profits is determined, based on the set of price points, the set of expected demand values, and subject to the supply-side, joining, and demand-side constraints. Based on the objective function, an optimal price profile for the item is provided.

Claims

1. A computer-implemented method for simultaneously pricing an item in a product hierarchy and allocating assets in a supply chain, the method comprising:

determining a set of price points for the item in the product hierarchy;

determining a value corresponding to a price elasticity of the item;

determining a value corresponding to a price elasticity of a correlated item;

prior to determining a set of expected demand values, determining a non-linear demand model based on a baseline demand of the item, the price elasticity of the item, a cross demand of the item created by the price elasticity of the correlated item, and a demand of the item created by a marketing instrument;

transforming the non-linear demand model into a linear demand model;

determining, by at least one processor associated with a computer system, a set of expected demand values for each price point for the item using the linear demand model, the value corresponding to the price elasticity of the item, the value corresponding to the price elasticity of the correlated item, and the set of price points, the set of expected demand values able to model demand uncertainty for each price point;

determining a supply-side constraint which models inventory, replenishment, and capacities associated with replenishment of the product hierarchy;

determining a joining constraint which requires that a selected set of expected demand values be equal to a net planned supply of the item;

determining a demand-side constraint;

determining an objective function, by the at least one processor associated with the computer system, to maximize revenue and minimize costs simultaneously based on the set of price points for the item, the set of expected demand values for the item, and subject to the supply-side constraint, the joining constraint, and the demand-side constraint; and

providing an optimal price profile for the item based on the objective function.

2. The method of claim 1, further comprising:

based on the objective function, providing optimal profits over a plan horizon for the item, optimal revenue for the item, optimal satisfied demand for the item, optimal demand-side costs and supply-side costs for the item, optimal quantity of replenishment supplies associated with the item, optimal inventory allocations for the item, optimal resource capacity consumption for the item, and optimal material capacity consumption for the item.

3. The method of claim 1, further comprising:

determining a supply-side cost incurred for the item, wherein the supply-side cost includes a cost of replenishment associated with the item, wherein the objective function is determined based on the supply-side cost.

4. The method of claim 1, further comprising:

determining a demand-side cost incurred for the item, wherein the demand-side cost models a cost associated with a change in price of the item, wherein the objective function is determined based on the demand-side cost.

5. The method of claim 1, wherein the objective function maximizes profits based on a set of supply-side slack variables which enforce a penalty for a usage of at least one of an unavailable resource quantity, an unmet demand quantity, an unavailable safety stock quantity, an unavailable external supply quantity, an unavailable storage volume capacity at an organization, an unavailable storage volume capacity on a carrier, and an unavailable weight capacity on the carrier.

6. The method of claim 1, wherein the item in the product hierarchy is a manufacturing item.

7. The method of claim 1, wherein the item in the product hierarchy is at least one of an end item, a component of an end item, and a product family.

8. The method of claim 1, wherein the set of price points are generated subject to a price profile rule.

9. The method of claim 1, wherein the cross demand of the item is determined based on an aggregation of a plurality of price elasticity values of the correlated item upon the item.

10. The method of claim 1, wherein the set of price points, the set of expected demand values, the supply-side constraint, the joining constraint, and the demand-side constraint are determined for each time phase, and wherein the optimal price profile for the item includes an optimal price per time phase.

11. The method of claim 1, further comprising:

generating supply metrics and demand metrics; and

determining the optimal price based on the supply metrics and the demand metrics.

12. A system for simultaneously pricing an item in a product hierarchy and allocating assets in a supply chain, the system comprising:

at least one processor;

a memory device operatively coupled to the processor;

a price point generator configured, by the at least one processor, to determine a set of price points for the item in the product hierarchy;

a demand generator configured, by the at least one processor, to determine a set of expected demand values for each price point for the item able to model demand uncertainty for each price point using a linear demand model, a value corresponding to a price elasticity of the item, a value corresponding to a price elasticity of a correlated item, and the set of price points, the linear demand model determined based at least in part upon a non-linear demand model transformation, the non-linear demand model determined based at least in part upon a baseline demand of the item, the price elasticity of the item, a cross demand of the item created by the price elasticity of a correlated item, and a demand of the item created by a marketing instrument;

an optimization constraint generator configured, by the at least one processor, to determine a supply-side constraint which models inventory, replenishment, and capacities associated with replenishment of the product hierarchy, a joining constraint which requires that a selected set of expected demand values be equal to a net planned supply of the item, and a demand-side constraint;

a model builder configured, by the at least one processor, to determine an objective function to maximize revenue and minimize costs simultaneously based on the set of price points for the item, the set of expected demand values for the item, and subject to the supply-side constraint, the joining constraint, and the demand-side constraint; and

a solver configured, by the at least one processor, to provide an optimal price profile for the item based on the objective function.

13. The system of claim 12, wherein the solver is further configured to provide, based on the objective function, optimal profits over a plan horizon for the item, optimal revenue for the item, optimal satisfied demand for the item, optimal demand-side costs and supply-side costs for the item, optimal quantity of replenishment supplies associated with the item, optimal inventory allocations for the item, optimal resource capacity consumption for the item, and optimal material capacity consumption for the item.

14. The system of claim 12, further comprising:

a cost computer configured to determine a supply-side cost incurred for the item, wherein the supply-side cost includes a cost of replenishment associated with the item, wherein the objective function is determined based on the supply-side cost.

15. The system of claim 12, further comprising:

a cost computer configured to determine a demand-side cost incurred for the item, wherein the demand-side cost models a cost associated with a change in price of the item, wherein the objective function is determined based on the demand-side cost.

16. The system of claim 12, wherein the objective function maximizes profits based on a set of supply-side slack variables which enforce a penalty for usage of at least one of an unavailable resource quantity, an unmet demand quantity, an unavailable safety stock quantity, an unavailable external supply quantity, an unavailable storage volume capacity at an organization, an unavailable storage volume capacity on a carrier, and an unavailable weight capacity on the carrier.

17. A computer program product stored on a non-transitory computer-readable medium having computer readable instructions embedded therein, which when executed by at least one processor, causing the at least one processor to implement a method for simultaneously pricing an item in a product hierarchy and allocating assets in a supply chain, the method comprising:

determining a set of price points for the item in the product hierarchy;

determining a value corresponding to the price elasticity of the item;

determining a value corresponding to the price elasticity of a correlated item;

prior to determining a set of expected demand values, determining a non-linear demand model based on a baseline demand of the item, the price elasticity of the item, a cross demand of the item created by the price elasticity of the correlated item, and a demand of the item created by a marketing instrument;

transforming the non-linear demand model into a linear demand model;

determining a set of expected demand values for each price point for the item using the linear demand model, the value corresponding to the price elasticity of the item, the value corresponding to the price elasticity of the correlated item, and the set of price points, the set of expected demand values able to model demand uncertainty for each price point;

determining a supply-side constraint which models inventory, replenishment, and capacities associated with replenishment of the product hierarchy;

determining a joining constraint which requires that a selected set of expected demand values be equal to a net planned supply of the item;

determining a demand-side constraint;

determining an objective function to maximize revenue and minimize costs simultaneously based on the set of price points for the item, the set of expected demand values for the item, and subject to the supply-side constraint, the joining constraint, and the demand-side constraint; and

providing an optimal price profile for the item based on the objective function.

18. The implemented method according to claim 17, further comprising:

providing optimal profits over a plan horizon for the item, optimal revenue for the item, optimal satisfied demand for the item, optimal demand-side costs and supply-side costs for the item, optimal quantity of replenishment supplies associated with the item, optimal inventory allocations for the item, optimal resource capacity consumption for the item, and optimal material capacity consumption for the item, based on the objective function.

19. The computer program product according to claim 17, wherein the objective function maximizes profits based on a set of supply-side slack variables which enforce a penalty for usage of at least one of an unavailable resource quantity, an unmet demand quantity, an unavailable safety stock quantity, an unavailable external supply quantity, an unavailable storage volume capacity at an organization, an unavailable storage volume capacity on a carrier, and an unavailable weight capacity on the carrier.