Offering a diverse product assortment brings numerous benefits to a firm, such as larger market share, increased customer satisfaction and more repeat purchases. However, determining the right assortment variety is difficult because products within the assortment maybe complementary or substitutes. Thus, the demand of each product depends on the availability of all other products in the assortment, complicating inventory planning and pricing decisions. A diverse product assortment also means that the demand is dispersed among a larger number of products, which may result in increased safety stocks and other additional operational expenses. Given these complex tradeoffs, how should a firm deal with the operational consequences of product variety and customer choice behavior? The goal of this project is to develop models and algorithms that allow decision-makers to offer the right assortment of products, stock the right amount of inventory, charge the right prices and sell to the right customer, while taking the customer choice behavior into consideration as they make these decisions. By studying the state-of-the-art choice models to capture customer choice behavior and by incorporating these choice models into critical operations management problems, the project offers an integrative framework to address assortment planning, inventory management, pricing and product personalization decisions.
The project covers various problems that apply to numerous industries, with focus on assortment planning, inventory management, pricing, and product personalization decisions. (a) One focus of the project is assortment decisions when inventories are not a limiting concern, which is appropriate when selling products that do not require consumption of physical inventories. The project will develop exact and approximate algorithms to find the right assortment of products to offer under various choice models. (b) Offered assortment determines the demands for the different products, indicating that assortment decisions interact with stocking decisions. The project will study models that make joint assortment offer and stocking decisions, as well as models that make assortment offer decisions under fixed inventory availability. Standard formulations of these models require a separate decision variable for each possible assortment, which can get too many. The goal is to develop compact formulations under different choice models and investigate the structural properties of the optimal assortment. (c) The project covers pricing decisions when customers choose among the offered products. The project will investigate such pricing problems under a variety of choice models and incorporate various constraints on the prices. (d) Developments in availability of real-time data on customers allow personalizing the assortment offering to each customer. The challenge is to quickly assess the choice behavior of each customer and compute the optimal assortment to offer in real time based on choice behavior and inventory availability. The project will study assortment personalization models with performance guarantees and investigate how to incorporate customer forecasts. (e) An important step in building models under customer choice is the estimation of the choice model that drives the choice behavior. The project will investigate how to estimate choice models from sales data. When data is limited, there is a tradeoff between increasing the predictive accuracy by using complex models and over-fitting complex choice models to limited data. An important goal is to derive robust criteria to balance the tradeoffs.
