Selective merchandise price optimization mechanism

1. A method for optimizing the prices of products for sale, comprising:

utilizing a computer-based scenario/results processor within an optimization server to present a sequence of data entry templates to a user, whereby the user specifies an optimization scenario, and whereby the user is enabled to prescribed and prioritize rules for the optimization scenario;

within the optimization server, optimizing the prices according to market demand for the products and demand chain costs for the products; said optimizing comprising:

estimating the market demand and calculating the demand chain costs for the products;

selectively limiting the number of prices that are optimized by said optimizing; and

up to a limit, progressively relaxing lower priority rules that contribute to a conflict in order to render said optimizing feasible; and

generating a plurality of optimization results templates and providing these templates to the user, wherein the optimum prices are presented.

2. The method as recited in claim 1 , wherein said utilizing comprises:

acquiring data corresponding to the optimization scenario from the user; and

formatting the data into a format suitable for performing a price optimization according to the optimization scenario.

3. The method as recited in claim 2 , wherein said acquiring comprises:

obtaining the data from the user over a data network that employs a packet-switched protocol.

4. The method as recited in claim 2 , wherein the data is acquired from a source electronic file that is designated by the user.

5. The method as recited in claim 1 , wherein the data entry templates and the optimization results templates are generated in hypertext markup language (HTML).

6. The method as recited in claim 1 , wherein the data entry templates and the optimization results templates are generated in extensible markup language (XML).

7. The method as recited in claim 1 , wherein the data entry templates and the optimization results templates are generated as Java applets.

8. The method as recited in claim 1 , wherein said utilizing comprises:

first providing a category template, for specifying a product category for price optimization, wherein the product category comprises a plurality of demand groups;

second providing a products template, for specifying the products for sale for which the optimum prices are to be determined, wherein the products for sale span more than one of the plurality of demand groups; and

third providing a time horizon template, for prescribing a time period for which the optimum prices are to be determined.

9. The method as recited in claim 8 , wherein said utilizing further comprises:

fourth providing a locations template, for prescribing a plurality of store groups for which the optimum prices are to be determined, wherein said prescribing directs said optimizing to utilize data corresponding to the plurality of said store groups when determining the optimum prices; and

fifth providing an at-large rules template, for specifying the rules to govern determination of the optimum prices, wherein the rules specify maximum allowable price swing for each of the products for sale, and maximum allowable swing for the average price of each demand group within the plurality of demand groups.

10. The method as recited in claim 9 , wherein said utilizing further comprises:

sixth providing a subset template, for prescribing a maximum number of price changes, whereby said selectively limiting determines the number of prices that are optimized.

11. The method as recited in claim 1 , wherein said utilizing comprises:

providing a strategy template, for specifying a merchandising performance figure of merit, and for prescribing limits for changes in sales volume.

12. The method as recited in claim 11 , wherein options for specifying the merchandising performance figure of merit comprise net profit, sales volume, and revenue.

13. The method as recited in claim 12 , wherein said generating comprises:

providing a price optimization results template, for supplying the user with scenario results corresponding to the optimization scenario, wherein the scenario results include optimized values and percent change values for merchandising factors, the merchandising factors including one or more of the following: sales volume, revenue, product cost, gross margin, and net profit.