System and method of assortment, space, and price optimization in retail store

• US 20070027745A1
• Filed: 07/28/2006
• Published: 02/01/2007
• Est. Priority Date: 07/28/2005
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Ouimet, Kenneth
• Granted Patent: US 8,032,406 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• 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

Abstract

A computer-implemented method involves modeling of product decisions in a retail store. The product decision variables are profit, assortment, placement, promotion, and inventory. Various rules and constraints such as facing elasticity, shelf replenishment costs, shelf space, carrying costs, facing capacity, slotting fees, and cannibalization are defined for multiple product decision variables. An objective function utilizes the rules and constraints for the multiple product decision variables. The objective function model is resolved by uses nested loops to solve for a first variable, and then using the first variable to solve for a second variable. Each decision variable in the objective function is controllable by externally determined multipliers. The objective function simultaneously models each of the multiple product decision variables by iteratively resolving the objective function into values which optimize sales, revenue, and profit for the retail store. The model is output in graphic format.

Claims

1. A computer-implemented method of modeling product decisions in a retail store, comprising:

defining rules and constraints for multiple product decision variables;

providing an objective function that utilizes the rules and constraints for the multiple product decision variables; and

simultaneously modeling each of the multiple product decision variables by iteratively resolving the objective function into values which optimize sales, revenue, and profit for the retail store.

2. The computer-implemented method of claim 1, wherein the product decision variables include profit, assortment, placement, promotion, and inventory.

3. The computer-implemented method of claim 1, wherein the rules and constraints are selected from the group consisting of facing elasticity, shelf replenishment costs, shelf space, carrying costs, facing capacity, slotting fees, and cannibalization.

4. The computer-implemented method of claim 1, wherein the objective function is given as

max θ

[{x}]=π

[{x}, {us}]+λ

^dsDS[{us}]+λ

^imPI[{x}]+λ

A^saSA[{x}].

5. The computer-implemented method of claim 1, wherein each decision variable in the objective function is controllable by externally determined multipliers.

6. The computer-implemented method of claim 1, wherein the model is output in graphic format.

7. The computer-implemented method of claim 1, wherein the objective function model is resolved by using nested loops to solve for a first variable and then using the first variable to solve for a second variable.

8. A method of modeling product decision variables in a retail environment, comprising:

defining rules and constraints for a plurality of product decision variables;

providing an objective function in terms of the rules and constraints for the plurality of product decision variables; and

simultaneously modeling each of the plurality of product decision variables by resolving the objective function into values which optimize sales, revenue, and profit for the retail store.

9. The method of claim 8, wherein the product decision variables include profit, assortment, placement, promotion, and inventory.

10. The method of claim 8, wherein the rules and constraints are selected from the group consisting of facing elasticity, shelf replenishment costs, shelf space, carrying costs, facing capacity, slotting fees, and cannibalization.

11. The method of claim 8, wherein the objective function is given as max θ

[{x}]=π

[{x}, {us}]+λ

^dsDS[{us}]+λ

^imPI[{x}]+λ

A^saSA[{x}].

12. The method of claim 8, wherein each decision variable in the objective function is controllable by externally determined multipliers.

13. The method of claim 8, wherein the model is output in graphic format.

14. The method of claim 8, wherein the objective function model is iteratively resolved by using nested loops to solve for a first variable and then using the first variable to solve for a second variable.

15. A computer program product usable with a programmable computer processor having a computer readable program code embodied therein, comprising:

computer readable program code which defines rules and constraints for a plurality of product decision variables;

computer readable program code which provides an objective function in terms of the rules and constraints for the plurality of product decision variables; and

computer readable program code which simultaneously models each of the plurality of product decision variables by resolving the objective function into values which optimize sales, revenue, and profit for the retail store.

16. The computer program product of claim 15, wherein the product decision variables include profit, assortment, placement, promotion, and inventory.

17. The computer program product of claim 15, wherein the rules and constraints are selected from the group consisting of facing elasticity, shelf replenishment costs, shelf space, carrying costs, facing capacity, slotting fees, and cannibalization.

18. The computer program product of claim 15, wherein the objective function is given as

max θ

[{x}]=π

[{x}, {us}]+λ

^dsDS[{us}]+λ

^imPI[{x}]+λ

A^saSA[{x}].

19. The computer program product of claim 15, wherein each decision variable in the objective function is controllable by externally determined multipliers.

20. A computer system for modeling product decision variables in a retail environment, comprising:

means for defining rules and constraints for a plurality of product decision variables;

means for providing an objective function in terms of the rules and constraints for the plurality of product decision variables; and

means for simultaneously modeling each of the plurality of product decision variables by resolving the objective function into values which optimize sales, revenue, and profit for the retail store.

21. The computer system of claim 20, wherein the product decision variables include profit, assortment, placement, promotion, and inventory.

22. The computer system of claim 20, wherein the rules and constraints are selected from the group consisting of facing elasticity, shelf replenishment costs, shelf space, carrying costs, facing capacity, slotting fees, and cannibalization.

23. The computer system of claim 20, wherein the objective function is given as

max θ

[{x}]=π

[{x}, {us}]+λ

^dsDS[{us}]+λ

^imPI[{x}]+λ

A^saSA[{x}].

24. The computer system of claim 20, wherein each decision variable in the objective function is controllable by externally determined multipliers.