Assortment decisions

• US 20020147630A1
• Filed: 04/04/2001
• Published: 10/10/2002
• Est. Priority Date: 04/04/2001
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Woo, Jonathan W., Leven, Steven M., Rose, Dawn M.
• Granted Patent: US 7,006,981 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• 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 20/203 Inventory monitoring
• 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

Abstract

Historical information is stored about demand behavior associated with different sizes of styles of items of commerce. A size assortment recommendation is generated based on the stored historical information and on objectives with respect to purchase or allocation decisions about assortments of the different sizes.

Claims

1. A method comprising

storing historical information about demand behavior associated with different sizes of styles of items of commerce, and

generating a size assortment recommendation based on the stored historical information and on objectives with respect to purchase or allocation decisions about assortments of the different sizes.

2. The method of claim 1 also including

generating the size assortment recommendation based also on operational constraints associated with the purchase or allocation decisions.

3. The method of claim 1 in which the historical information is stored separately for each style offered at each of multiple commerce locations.

4. The method of claim 1 in which the historical information is stored separately for each style offered in multiple time periods.

5. The method of claim 1 in which the generation of a size assortment recommendation includes determining size profiles each representing a distribution of percentages of sales by size within one of the styles.

6. The method of claim 2 in which the operational constraints include a limitation on the granularity of purchase and allocation decisions that can be implemented.

7. The method of claim 6 in which the generation of a size assortment recommendation includes

determining size profiles for each representing a distribution of percentages of sales by size within one of the styles, and

optimizing the number of size profiles by assigning them to groups.

8. The method of claim 7 also including substituting a representative size profile for all of the size profiles of each of the groups.

9. The method of claim 7 in which the optimization is based on the objectives.

10. The method of claim 9 in which the optimization is based on a genetic algorithm.

11. The method of claim 7 in which the optimization yields a determination of the optimal number of groups.

12. The method of claim 1 in which the size assortment recommendation is an optimal assortment, and also including performing simulations on the optimal assortment to derive a risk profile of outcomes.

14. The method of claim 5 also including

generating error bound information from size profiles for multiple styles and multiple time periods.

15. The method of claim 14 also including

using the error bound information to optimize size profiles based on the objectives.

16. The method of claim 1 in which the determination of size profiles includes

filtering historical data to data representative of a supply-unconstrained environment.

17. The method of claim 16 in which the filtering includes eliminating data for styles for which not all sizes are present for a given commerce location and a given time period.

13. A method comprising

displaying to a user recommended size profiles for allocating a style of an item of commerce to retail outlets based on a predefined objective function, and

enabling the user to specify alterations in an analysis that formed a basis of the recommended size profiles, and

displaying to the user the impact of the alterations in terms of sales dollars, gross profit, size break week, or stockout week.