Real-time Dynamic Optimization for Omnichannel Retailers

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This project seeks to use mathematical models and analysis to identify near-optimal algorithms for dynamic optimization problems arising in omnichannel retail. The central theme is to help omnichannel retailers understand how various decisions (e.g. pricing, assortment and inventory) should be made when order fulfillment (i.e. physical delivery of products to customers) is costly. Omnichannel retail refers to a fully integrated approach where customers are provided with a unified shopping experience across multiple channels. It is fast emerging as the default mode for retail operations. To enable a seamless shopping experience with multiple touchpoints, omnichannel retailers endure significant operational costs from order fulfillment activities. Moreover, correlated decisions need to be optimized jointly to maximize profits. The increasing practical importance of this area and the sparsity of academic literature present exciting research opportunities that will be directly impactful in a broad sense. Motivated by recent innovations in omnichannel retail, we will investigate the following three closely related research questions that have not been addressed in the literature: 1. What is the value of delayed order fulfillment? 2. How can retailers jointly optimize delivery option offerings and order fulfillment decisions? 3. How do inflexibilities in the fulfillment process affect retailer's pricing and inventory decisions? Our research will provide solutions that align theory with business practices, and highlight the benefit and feasibility of joint dynamic optimization. From a theoretical perspective, our problems are modelled as stochastic control problems that are intractable to solve optimally. We plan to propose algorithms that are easily implementable in real-time and have near-optimal performance guarantees. As the problems involve novel dynamics that have not been studied before, this project broadly contributes to stochastic control and online algorithm literature. Five HQPs will gain a skillset that is highly in demand in Operations and Data Analytics that is essential to address relevant real-world problems and for success in an academic or industry career. From a practical perspective, this project timely addresses common challenges faced by omnichannel retailers, who rely on complicated logistic networks to serve customers with high service requirements. Our algorithms will be tested using real data and have the potential of being implemented by industry partners in the retail sector (e.g. Amazon, Oracle Labs, and Instacart). Driven by the rise of Generation Z and the impact of the COVID-19 pandemic, consumer shopping habits have changed drastically. The research outcomes will benefit the broader retail industry in embracing these changes. The potential economic impact on Canadian retail sectors will be significant, since even a moderate improvement of 1% in revenue could translate to over CAD 5B of additional sales.