Knowledge assisted pricing advisor for large-scale retailers: KAPA

Abstract

It is very difficult for the large-scale retailers, who deal with tens of thousands of items, to price all the items dynamically reflecting all the constraints and policies. In spite of its importance, the prices are determined by human experts because the process of setting the prices of all the items is not established yet. To solve this problem, we adopt a mixed model that combines three typical pricing models: cost-plus model, competition-oriented model, and demand-oriented model. Since each model can be corrverted to a set of constraints in point and interval forms, solving the pricing problem with the three groups of models requires an algorithm which can solve the problems with weighted constraints of intervals and points. So we have devised an algorithm named Point Determination Algorithm. From the rules that represents the models, the constraints are extracted to be solvable by the Point Determination Algorithmn. A prototype KAPA (Knowledge Assisted Pricing Advisor) is developed with this idea using the expert system ervironment UNIK - a tool developed by KAIST. According to the experiment with 76 items in comparison with 53 human pricing experts, we confirmed that the KAPA can perform highly consissant with human experts. This implies KAPA system is applicable to pricing milions of items dynamically.