Generating an optimized price schedule for a product

Abstract

Generating a price schedule involves generating a graph (50) having paths that include states (52) with values (54, 56, 58). The graph (50) is generated by determining the values (56, 58) of a successor state (52) from the values (56, 58) of a predecessor state (52). An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. An best-fitting elasticity curve is selected. Adjusting a demand forecast value (56) includes estimating an inventory and a demand at a number of locations (24). An expected number of unrealized sales at each location (24) is calculated. An sales forecast value (56) is determined according to the expected number.

Description

RELATED APPLICATION [0001] This application claims benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application Ser. No. 60 / 238,676, filed Oct. 6, 2000.TECHNICAL FIELD OF THE INVENTION [0002] This invention relates in general to commercial environments, and more particularly to generating an optimized price schedule for a product. BACKGROUND OF THE INVENTION [0003] An important decision for a business to make is determining the prices of products, goods, or services offered by the business. Pricing decisions are important to a business's success or failure, since these decisions may have a critical impact on customer demand, profitability, and business operations. Informed pricing decisions, however, may be difficult to make, since these decisions may need to reflect a large number of business objectives and operating constraints, and may need to be made for a large number of items across a large number of sales locations. Moreover, pricing decisions may need to be frequently up...

AI Technical Summary

Benefits of technology

[0005] In accordance with the present invention, disadvantages and problems associated with generating price schedules have been substantially reduced or eliminated.

[0009] Certain embodiments of the present invention may provide one or more technical advantages over previous price scheduling techniques. Selling goods typically requires dynamic adjustment of a sales price over a time horizon. Given a forecast of product demand over time and a model of price elasticity, the present invention may calculate a price schedule that maximizes a business objective such as cumulative revenue, while satisfying business constraints such as a desired price behavior or an inventory target level.

[0013] The present invention may efficiently generate an optimized price schedule using a “quantized dynamic programming” technique. A transition graph with paths representing possible price schedules may be generated using quantized dynamic programming. An optimal path of the transition graph is selected in order to determine an optimized price schedule. The present invention may improve the efficiency of the optimization by quantizing values associated with the transition graph at various degrees of granularity. Quantizing the state values reduces the size of transition graph, which may improve the processing time and power required to determine an optimal path of the transition graph.

[0015] The present invention may be used to compute an optimal sequence of product markdowns for liquidating merchandise, which may enable sellers to better plan and more profitably manage inventory, such as at the end of a product lifecycle. Given initial inventory and price values, a price schedule of product markdowns that maximizes business objectives while satisfying business constraints may be calculated. The price schedule may be used to maximize margin capture rates while reducing margin degradation and to provide accurate estimates of both margin capture rates and margin degradation factors. In addition, the present invention may be used to determine optimal start and end dates of a markdown horizon, or to determine which products should be put on a markdown plan. The present invention may be used to guide the decision of how many units of which product should be acquired for subsequent sale.

Problems solved by technology

Informed pricing decisions, however, may be difficult to make, since these decisions may need to reflect a large number of business objectives and operating constraints, and may need to be made for a large number of items across a large number of sales locations.

Optimizing price schedules, however, may be computationally difficult.

As a result, previous techniques for generating an optimized price schedule have been inadequate.

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