A Model of Competitive Assortment Planning Algorithm

TL;DR

This paper introduces a novel assortment planning algorithm that incorporates Bayesian updating and two-stage optimization to promote competition and prevent collusion in digital marketplaces.

Contribution

It proposes a new model and algorithm for assortment planning that maintains competitiveness and maximizes revenue while avoiding collusion.

Findings

Collusive assortment can increase purchase likelihood but not maximize revenue.

The proposed algorithm sustains competition and optimizes expected revenue.

The model addresses competition and collusion in digital marketplaces.

Abstract

With a novel search algorithm or assortment planning or assortment optimization algorithm that takes into account a Bayesian approach to information updating and two-stage assortment optimization techniques, the current research provides a novel concept of competitiveness in the digital marketplace. Via the search algorithm, there is competition between the platform, vendors, and private brands of the platform. The current paper suggests a model and discusses how competition and collusion arise in the digital marketplace through assortment planning or assortment optimization algorithm. Furthermore, it suggests a model of an assortment algorithm free from collusion between the platform and the large vendors. The paper's major conclusions are that collusive assortment may raise a product's purchase likelihood but fail to maximize expected revenue. The proposed assortment planning, on the other hand, maintains competitiveness while maximizing expected revenue.