Dynamic pricing modeling and inventory management in omnichannel retail using Quantum Decision Theory and reinforcement learning

Abstract

In the world of omnichannel retail, where customers seamlessly switch between online and offline channels, pricing and inventory management decisions have become more complex than ever. Customer purchasing behavior is influenced by uncertainty, market fluctuations, and competitive interactions, which traditional models fail to accurately predict. In such conditions, the need for intelligent and adaptive decision-making frameworks is more critical than ever. For the first time, this study presents a novel approach combining Quantum Decision Theory, Quantum Markov Chains (QMC), Quantum Dynamic Games, and Reinforcement Learning to optimize dynamic pricing and inventory management. By leveraging concepts such as superposition, observer effect, and quantum interference, the proposed model overcomes the limitations of classical models and provides a deeper understanding of customer behavior in uncertain environments. Additionally, a Quantum Multi-Level Markov Process (QMLMP) is employed to model market variations and enhance predictions. The results of this study demonstrate that the innovative model improves the accuracy of purchase behavior predictions, optimizes pricing and inventory management strategies, and helps retailers make more competitive and profitable decisions. This research introduces a transformative approach to tackling retail challenges in the digital age and paves the way for future studies in this domain.