Soutenance de thèse : Minh Tam TRAN

Innovative multichannel models for pricing and inventory decisions considering service level

Doctorante : Minh Tam TRAN

Laboratoire INSA : DISP

École doctorale : ED512 : InfoMaths (Informatique et Mathématiques de Lyon)

The thesis investigates contemporary challenges in retail management amidst the digital revolution, with a focus on multichannel retailing, dual-channel pricing, and data-driven inventory management. This thesis first begins with an overview of evolving retail dynamics driven by technological advancements and shifting consumer demands, emphasizing the necessity for inventive solutions to navigate these complexities. Second, by exploring multichannel retailing in-depth, the study examines inventory allocation and pricing optimization across physical and online channels. It addresses a multichannel pricing problem, proposing a methodology to ensure optimal solutions and highlighting the importance of channel coordination and service levels on market share and profitability. Thirdly, further delving into dual-channel pricing, the thesis presents a novel pricing model capturing intricate interactions between channels, retailers, and customers. It emphasizes the significance of determining optimal physical store capacity and managing stock-out conversions to online sales with promotions. Fourth, introducing data-driven inventory management methodologies, the study leverages Kernel Density Estimation (KDE) within chance-constrained optimization frameworks. By demonstrating superior performance in achieving target service levels compared to traditional methods, the thesis emphasizes the importance of managing inventory under uncertainty while maintaining service quality. Last but not least, the thesis concludes by promoting a deeper understanding of retail management in the digital age, offering valuable insights and methodologies to navigate modern retailing complexities. By embracing innovation, data-driven approaches, and customer-centric strategies, retailers can position themselves for success in an increasingly dynamic and competitive environment. Future research directions include exploring advanced machine learning techniques and extending the model to consider sustainability and supply chain resilience.