Browsing by Author "Bernstein, Fernando"
- Results Per Page
- Sort Options
Item Embargo Customer Choice Models in Emerging Retail Markets(2023) Guo, YuanIn this dissertaion we discuss customer choice and company decisions in three emerging retail settings. We first consider subscription box services in which a provider selects the assortment of products to include in the box by taking into account the customer's preferences. Customers interested in purchasing a product choose between engaging in searching physical stores or subscribing to a box delivery service. We study the subscription box company's problem of selecting the optimal contents of the box to maximize expected revenue (by driving demand from customers). We model the interaction between customer search and subscription by applying a cross-nested logit framework that correlates the contents in the box with the products available at the stores. We find that if a preview of the box is available, for customers with intermediate values of the search cost, it may be optimal to include a so-called utility loss leader, i.e., a product with relatively low valuation, to entice customers to subscribe to the box delivery service and therefore increase the likelihood of a sale. Next, we study companies' problem selling in social media, where brands attach product tags directly in their posts and users can complete their purchase by clicking on these tags. We propose a novel choice model that captures the users' impulsive behavior and limited attention span while browsing products in social media platforms. We consider a seller's product display problem on a product page and examine different strategies to sell through social media. We find that, depending on the product preferences and the users' pattern of attention decay, the optimal display set can have different structure. Finally, we discuss the fashion retailer's production problem selling in a market where a secondhand platform collects old clothes from customers and resells them. We construct a customer decision model that captures their consumption of new and/or used products under transitioning fashion trends over time. We find that if the platform operates independently and the hassle cost related to buying and selling used goods is high, the retailer's optimal production decision will not be affected by the existence of the secondhand market.
Item Open Access Data-Driven Learning Models with Applications to Retail Operations(2018) Modaresi, SajadData-driven approaches to decision-making under uncertainty is at the center of many operational problems. These are problems in which there is an element of uncertainty (e.g., customer demand) that needs to be estimated (learned) from data (e.g., customer transaction data) in order to make online (dynamic) operational (e.g., assortment) decisions. This dissertation adopts a data-driven active learning approach to study various operational problems under uncertainty with a focus on retail operations.
The first two essays in this dissertation study the classic exploration (i.e., parameter estimation) versus exploitation (i.e., optimization) trade-off from different perspectives. The first essay takes a theoretical approach and studies such trade-off in a combinatorial optimization setting. We show that resolving the exploration versus exploitation trade-off efficiently is related to solving a Lower Bound Problem (LBP), which simultaneously answers the questions of what to explore and how to do so. We establish a fundamental limit on the asymptotic performance of any admissible policy that is proportional to the optimal objective value of the LBP problem. We also propose near-optimal policies that are implementable in real-time. We test the performance of the proposed policies through extensive numerical experiments and show that they significantly outperform the relevant benchmark.
The second essay considers the dynamic assortment personalization problem of an online retailer facing heterogeneous customers with unknown product preferences. We propose a prescriptive approach, called the dynamic clustering policy, for dynamic estimation of customer preferences and optimization of personalized assortments. We test the proposed approach with a case study based on a dataset from a large Chilean retailer. The case study suggests that the benefits of the dynamic clustering policy under the Multinomial Logit (MNL) model can be substantial and result (on average) in more than 37% additional transactions compared to a data-intensive policy that treats customers independently and in more than 27% additional transactions compared to a linear-utility policy that assumes that product mean utilities are linear functions of available customer attributes.
Further focusing on retail operations, the final essay studies the interplay between a retailer's return and pricing policies and customers' purchasing decisions. We characterize the retailer's optimal prices in the cases with and without product returns and determine conditions under which offering the return option to customers increases the retailer's revenue. We also explore the impact of accounting for product returns on demand estimation. The preliminary numerical results based on a real dataset suggest that our model, which accounts for product returns, increases demand estimation accuracy compared to models that do not consider product returns in their estimation.
Item Open Access Impact of Prices on Inventory Systems: Theory and Emerging Issues(2013) Li, YangFirms' inventory or production decisions are influenced by a variety of factors, including both the selling price of the end products and the purchasing cost of raw materials. In most cases, there is a strong connection between purchasing costs and selling prices. In my dissertation, I study the impact of prices on a firm's inventory
decisions, particularly in systems with delivery lead time and environmental concerns. The findings are reported in three studies. The first study analyzes the joint inventory and pricing problem with lead time, which is known to be difficult to solve due to its computational complexity. We develop a simple heuristic to resolve
the computational issue and reveal the impact of lead time on the joint decisions. In the second study, we extend the heuristic approach in the previous study to systems with both positive lead time and fixed ordering costs. The effectiveness of the heuristic in both studies are verified through both theoretical bounds and numerical experiments. In the third study, we examine the effect of the procurement cost and its volatility on a firm's profit. This allows us to study under what conditions a firm can profitably operate an eco-friendly supply chain. Our study also helps the firms to understand what type of products would better absorb the higher costs associated with an eco-friendly production system.